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 "a survey of sparse representation algorithms and applications"):
  Abstract: Sparse representation has attracted much attention from researchers in fields of signal processing, image processing, computer vision, and pattern recognition. Sparse representation also has a good reputation in both theoretical research and practical applications. Many different algorithms have been proposed for sparse representation. The main purpose of this paper is to provide a comprehensive study and an updated review on sparse representation and to supply guidance for researchers. The taxonomy of sparse representation methods can be studied from various viewpoints. For example, in terms of different norm minimizations used in sparsity constraints, the methods can be roughly categorized into five groups: 1) sparse representation with    $l_{0}$   -norm minimization; 2) sparse representation with    $l_{p}$   -norm (   $0  ) minimization; 3) sparse representation with    $l_{1}$   -norm minimization; 4) sparse representation with    $l_{2,1}$   -norm minimization; and 5) sparse representation with    $l_{2}$   -norm minimization. In this paper, a comprehensive overview of sparse representation is provided. The available sparse representation algorithms can also be empirically categorized into four groups: 1) greedy strategy approximation; 2) constrained optimization; 3) proximity algorithm-based optimization; and 4) homotopy algorithm-based sparse representation. The rationales of different algorithms in each category are analyzed and a wide range of sparse representation applications are summarized, which could sufficiently reveal the potential nature of the sparse representation theory. In particular, an experimentally comparative study of these sparse representation algorithms was presented.

Includes members:
  Member "group based sparse representation for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "l_ 1 2 regularization convergence of iterative half thresholding algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix completion via nonconvex regularization convergence of the proximal gradient algorithm")
  Member "constructing a non negative low rank and sparse graph with data adaptive features":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised sparse representation based classification for face recognition with insufficient labeled samples")
  Member "sparse recovery of streaming signals using l1 homotopy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recursive recovery of sparse signal sequences from compressive measurements a review")
  Member "compressed sensing signal recovery via forward backward pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving a omp")
  Member "an mdl framework for sparse coding and dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning simple thresholded features with sparse support recovery")
  Member "image super resolution via dual dictionary learning and sparse representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "globally variance constrained sparse representation for image set compression")
  Member "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving robustness of deep learning based image reconstruction")

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)