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: "robust face recognition via block sparse bayesian learning"
  Abstract: Face recognition (FR) is an important task in pattern recognition and computer vision. Sparse representation (SR) has been demonstrated to be a powerful framework for FR. In general, an SR algorithm treats each face in a training dataset as a basis function, and tries to find a sparse representation of a test face under these basis functions. The sparse representation coefficients then provide a recognition hint. Early SR algorithms are based on a basic sparse model. Recently, it has been found that algorithms based on a block sparse model can achieve better recognition rates. Based on this model, in this study we use block sparse Bayesian learning (BSBL) to find a sparse representation of a test face for recognition. BSBL is a recently proposed framework, which has many advantages over existing block-sparse-model based algorithms. Experimental results on the Extended Yale B, the AR and the CMU PIE face databases show that using BSBL can achieve better recognition rates and higher robustness than state-of-the-art algorithms in most cases.

Connected through hyperedge HE-1 (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"
    - "sparsity and robustness in face recognition"

Connected through hyperedge HE-2 (induced by source paper "rectified gaussian scale mixtures and the sparse non negative least squares problem"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "expectation maximization gaussian mixture approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-3 (induced by source paper "robust bayesian method for simultaneous block sparse signal recovery with applications to face recognition"):
  with members:
    - "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-4 (induced by source paper "paradigm shift in continuous signal pattern classification mobile ride assistance system for two wheeled mobility robots"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "collaborative representation based classification for face recognition"
    - "sparse subspace clustering algorithm theory and applications"

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)