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 "enhanced low rank matrix approximation"):
  Abstract: This letter proposes to estimate low-rank matrices by formulating a convex optimization problem with nonconvex regularization. We employ parameterized nonconvex penalty functions to estimate the nonzero singular values more accurately than the nuclear norm. A closed-form solution for the global optimum of the proposed objective function (sum of data fidelity and the nonconvex regularizer) is also derived. The solution reduces to singular value thresholding method as a special case. The proposed method is demonstrated for image denoising.

Includes members:
  Member "scalable algorithms for tractable schatten quasi norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix completion with deterministic sampling theories and methods")
  Member "sparsity based algorithm for detecting faults in rotating machines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "repetitive transients extraction algorithm for detecting bearing faults")
  Member "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "convex denoising using non convex tight frame regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi focus image fusion using sparse representation and coupled dictionary learning")
  Member "a note on patch based low rank minimization for fast image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatial frequency domain nonlocal total variation for image denoising")
  Member "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformed ell_1 regularization for learning sparse deep neural networks")
  Member "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms")
  Member "patch based low rank minimization for image denoising":
    also connected through other hyperedges:
      (none)

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)