Self-attention-based　approaches　that　leverage　global　context　information　for　hyperspectral　image　(HSI)　classification　have　gained　increasing　prominence.　Nevertheless,　due　to　the　assignment　of　equivalent　attention　weight　to　all　the　tokens　(pixels　or　patches),　the　existing　self-attention　mechanism　inadvertently　prioritizes　the　nonlabel-specified　information　over　the　instinct　label-specified　information,　which　generates　attention　shifts　and　redundancy　in　HSI　classification.　To　alleviate　the　mentioned　barrier,　we　propose　the　center-specific　perception　transformer　(CP-Transformer)　network,　which　is　the　first　attempt　to　perform　class-guided　attention　and　filter　interference　factors　for　HSI　classification　feature　representation.　Specifically,　the　central-pixel　focus　attention　(CFA)　module　is　presented　to　compute　the　label-related　attention　between　the　center　and　other　pixels.　In　this　manner,　CFA　reduces　computational　complexity　and　closely　aligns　with　the　center-pixel　labeling　strategy.　Besides,　the　spectral　saliency　focus　attention　(SSFA)　module　is　developed　to　capture　the　spectral　correlation　by　focusing　salient　bands　to　provide　a　beneficial　supplement　for　spatial　features.　Moreover,　the　hierarchical　integration　network　(HIN)　constructs　the　inference　network　to　integrate　and　rectify　spatial-spectral　features　for　HSI　classification.　The　experiment　results　on　four　popular　HSI　datasets　demonstrate　that　the　proposed　method　achieves　robust　performance　compared　to　other　state-of-the-art　methods.　Our　code　will　be　released　at　https://github.com/Chirsycy/CP-Transformer