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 "image restoration using group sparse representation via weighted nuclear norm minimization"):
  Abstract: As the matrix formed by nonlocal similar patches in a natural image is of a low rank, the nuclear norm minimization (NNM) has been widely studied for image processing. Since the singular values have clear meanings and should be treated differently, NNM regularizes each of them equally, which often restricts its capability and flexibility. Recent advances have suggested that the weighted nuclear norm minimization (WNNM) has shown great potential in different image restoration studies, where singular values are assigned different value. However, it still lacks a mathematical derivation why the weighted nuclear norm is more appropriate than the nuclear norm. In this paper, we proposed a new scheme for image restoration using group sparse representation via weighted nuclear norm minimization (GSR-WNNM). We show mathematically the advantage of WNNM, from a group sparse representation perspective, where GSR offers a powerful mechanism of combining local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. Then, an effective dictionary for each group is learned from the reconstructed image itself rather a large number of natural image dataset, ensuring a low computational complexity. Moreover, to further improve the computational efficiency of the proposed method, we have developed an implementation of fast convergence via the alternating direction method of multipliers (ADMM). Experimental results have shown that the proposed GSR-WNNM method significantly outperforms the state-of-the-art methods both quantitatively and qualitatively.

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 "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "image restoration using joint statistical modeling in a space transform domain":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "a fast patch dictionary method for whole image recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable linearized admm")
  Member "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving robustness of deep learning based image reconstruction")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)