human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "breast tumor classification based on decision information genes and inverse projection sparse representation"):
  Abstract: Microarray gene expression data-based breast tumor classification is an active and challenging issue. In this paper, a robust breast tumor recognition framework is presented based on considering reducing clinical misdiagnosis rate and exploiting available information in existing samples. A wrapper gene selection method is established from a new perspective of reducing clinical misdiagnosis rate. The further feature selection of information genes is fulfilled by a modified NMF model, which is motivated by hierarchical learning and layer-wise pre-training strategy in deep learning. For completing the classification, an inverse projection sparse representation (IPSR) model is constructed to exploit information embedded in existing samples, especially in test ones. Moreover, the IPSR model is optimized via generalized ADMM and the corresponding convergence is analyzed. Extensive experiments on three public breast tumor datasets show that the proposed method is stable and effective for breast tumor classification. Compared to the latest literature, there is 14% higher in classification accuracy. Specificity and sensitivity achieve 94.17% and 97.5%.

Includes members:
  Member "regularized robust coding for face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "biometric recognition using deep learning a survey")
  Member "gene selection with guided regularized random forest":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization")
  Member "non negative sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed bayesian matrix decomposition for big data mining and clustering")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CV(Computer Vision and Pattern Recognition)