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 "dual reweighted lp norm minimization for salt and pepper noise removal"):
  Abstract: The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision. It is usually replaced by the principal component pursuit (PCP) model in order to pursue the convex property, leading to the undesirable overshrink problem. In this paper, we propose a dual weighted lp-norm (DWLP) model with a more reasonable weighting rule and weaker powers, which greatly generalizes the previous work and provides a better approximation to the rank minimization problem for original matrix as well as the l0-norm minimization problem for sparse data. Moreover, an approximate closed-form solution is introduced to solve the lp-norm minimization, which has more stability in the nonconvex optimization and provides a more accurate estimation for the low-rank and sparse matrix recovery problem. We then apply the DWLP model to remove salt-and-pepper noise by exploiting the image nonlocal self-similarity. Both qualitative and quantitative experiments demonstrate that the proposed method outperforms other state-of-the-art methods. In terms of PSNR evaluation, our DWLP achieves about 7.188dB, 5.078dB, 3.854dB, 2.536dB and 0.158dB improvements over the current WSNM-RPCA under 10\% to 50\% salt-and-pepper noise with an interval 10\% respectively.

Includes members:
  Member "bridge the gap between group sparse coding and rank minimization via adaptive dictionary learning":
    also connected through other hyperedges:
      (none)
  Member "image denoising using group sparsity residual and external nonlocal self similarity prior":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "kernel free image deblurring with a pair of blurred noisy images")
  Member "weighted schatten p norm minimization for image denoising and background subtraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "analyzing the group sparsity based on the rank minimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "image denoising using new adaptive based median filters":
    also connected through other hyperedges:
      (none)
  Member "generalized singular value thresholding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "stable principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  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)