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: "collaborative representation based classification for face recognition"
  Abstract: By coding a query sample as a sparse linear combination of all training samples and then classifying it by evaluating which class leads to the minimal coding residual, sparse representation based classification (SRC) leads to interesting results for robust face recognition. It is widely believed that the l1- norm sparsity constraint on coding coefficients plays a key role in the success of SRC, while its use of all training samples to collaboratively represent the query sample is rather ignored. In this paper we discuss how SRC works, and show that the collaborative representation mechanism used in SRC is much more crucial to its success of face classification. The SRC is a special case of collaborative representation based classification (CRC), which has various instantiations by applying different norms to the coding residual and coding coefficient. More specifically, the l1 or l2 norm characterization of coding residual is related to the robustness of CRC to outlier facial pixels, while the l1 or l2 norm characterization of coding coefficient is related to the degree of discrimination of facial features. Extensive experiments were conducted to verify the face recognition accuracy and efficiency of CRC with different instantiations.

Connected through hyperedge HE-1 (induced by source paper "adaptive locality preserving regression"):
  with members:
    - "joint intermodal and intramodal label transfers for extremely rare or unseen classes"
    - "deep linear discriminant analysis"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "robust classification with sparse representation fusion on diverse data subsets"):
  with members:
    - "a survey of sparse representation algorithms and applications"

Connected through hyperedge HE-3 (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"
    - "sparse subspace clustering algorithm theory and applications"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-4 (induced by source paper "kernel cross view collaborative representation based classification for person re identification"):
  with members:
    - "re ranking person re identification with k reciprocal encoding"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification for real world surveillance systems"
    - "learning a discriminative null space for person re identification"
    - "deep ranking for person re identification via joint representation learning"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "a siamese long short term memory architecture for human re identification"
    - "an enhanced deep feature representation for person re identification"

Connected through hyperedge HE-5 (induced by source paper "dictionary integration using 3d morphable face models for pose invariant collaborative representation based classification"):
  with members:
    - "fitting 3d morphable models using local features"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "spectral collaborative representation based classification for hand gestures recognition on electromyography signals"):
  with members:
    - "paradigm shift in continuous signal pattern classification mobile ride assistance system for two wheeled mobility robots"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-7 (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"
    - "robust face recognition via block sparse bayesian learning"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-8 (induced by source paper "continuous and simultaneous gesture and posture recognition for commanding a robotic wheelchair towards spotting the signal patterns"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "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)