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: "constructing a non negative low rank and sparse graph with data adaptive features"
  Abstract: This paper aims at constructing a good graph for discovering intrinsic data structures in a semi-supervised learning setting. Firstly, we propose to build a non-negative low-rank and sparse (referred to as NNLRS) graph for the given data representation. Specifically, the weights of edges in the graph are obtained by seeking a nonnegative low-rank and sparse matrix that represents each data sample as a linear combination of others. The so-obtained NNLRS-graph can capture both the global mixture of subspaces structure (by the low rankness) and the locally linear structure (by the sparseness) of the data, hence is both generative and discriminative. Secondly, as good features are extremely important for constructing a good graph, we propose to learn the data embedding matrix and construct the graph jointly within one framework, which is termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive experiments on three publicly available datasets demonstrate that the proposed method outperforms the state-of-the-art graph construction method by a large margin for both semi-supervised classification and discriminative analysis, which verifies the effectiveness of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples"):
  with members:
    - "symmetric non rigid structure from motion for category specific object structure estimation"
    - "exploiting symmetry and or manhattan properties for 3d object structure estimation from single and multiple images"
    - "sparse illumination learning and transfer for single sample face recognition with image corruption and misalignment"
    - "deeply learned face representations are sparse selective and robust"
    - "dynamic updating for ell_ 1 minimization"

Connected through hyperedge HE-2 (induced by source paper "a survey of sparse representation algorithms and applications"):
  with members:
    - "group based sparse representation for image restoration"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "an mdl framework for sparse coding and dictionary learning"
    - "image super resolution via dual dictionary learning and sparse representation"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "fast l1 minimization algorithms for robust face recognition"

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)