human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sparsity and robustness in face recognition"
  Abstract: This report concerns the use of techniques for sparse signal representation and sparse error correction for automatic face recognition. Much of the recent interest in these techniques comes from the paper "Robust Face Recognition via Sparse Representation" by Wright et al. (2009), which showed how, under certain technical conditions, one could cast the face recognition problem as one of seeking a sparse representation of a given input face image in terms of a "dictionary" of training images and images of individual pixels. In this report, we have attempted to clarify some frequently encountered questions about this work and particularly, on the validity of using sparse representation techniques for face recognition.

Connected through hyperedge HE-1 (induced by source paper "connections between nuclear norm and frobenius norm based representations"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "a unified framework for representation based subspace clustering of out of sample and large scale data"
    - "learning locality constrained collaborative representation for robust face recognition"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "collaborative representation based classification for face recognition"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-2 (induced by source paper "relevance subject machine a novel person re identification framework"):
  with members:
    - "robust bayesian method for simultaneous block sparse signal recovery with applications to face recognition"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "top push video based person re identification"
    - "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "robust face recognition via block sparse bayesian learning"

Connected through hyperedge HE-3 (induced by source paper "collaborative representation based classification for face recognition"):
  with members:
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "fast l1 minimization algorithms for robust face recognition"

Connected through hyperedge HE-4 (induced by source paper "fast l1 minimization algorithms for robust face recognition"):
  with members:
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "bayesian compressive sensing via belief propagation"

Question: Please tell me which class the center node 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)