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 benchmark for sparse coding when group sparsity meets rank minimization"):
  Abstract: Sparse coding has achieved a great success in various image processing tasks. However, a benchmark to measure the sparsity of image patch/group is missing since sparse coding is essentially an NP-hard problem. This work attempts to fill the gap from the perspective of rank minimization. More details please see the manuscript....

Includes members:
  Member "image restoration by iterative denoising and backward projections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for single image super resolution a brief review")
  Member "random walk graph laplacian based smoothness prior for soft decoding of jpeg images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast graph metric learning via gershgorin disc alignment")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  Member "enhanced low rank matrix approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "weighted schatten p norm minimization for image denoising and background subtraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension")
  Member "analyzing the group sparsity based on the rank minimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rank minimization for snapshot compressive imaging")
  Member "marlow a joint multiplanar autoregressive and low rank approach for image completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a comprehensive survey for low rank regularization")
  Member "partial sum minimization of singular values in robust pca algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm")

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)