Distinguishing vulnerable code from non-vulnerable code is challenging due to high inter-class similarity. Supervised contrastive learning (SCL) improves embedding separation but struggles with intra-class clustering, especially when variations within the same class are subtle. We propose Cluster-Enhanced Supervised Contrastive Loss (CESCL), an extension of SCL with a distance-based regularization term that tightens intra-class clustering while maintaining inter-class separation. Evaluating on CodeBERT and GraphCodeBERT with Binary Cross Entropy (BCE), BCE + SCL, and BCE + CESCL, our method improves F1 score by 1.76% on CodeBERT and 4.1% on GraphCodeBERT, demonstrating its effectiveness in code vulnerability detection and broader applicability to high-similarity classification tasks.