human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "group sparse signal denoising non convex regularization convex optimization"
  Abstract: Convex optimization with sparsity-promoting convex regularization is a standard approach for estimating sparse signals in noise. In order to promote sparsity more strongly than convex regularization, it is also standard practice to employ non-convex optimization. In this paper, we take a third approach. We utilize a non-convex regularization term chosen such that the total cost function (consisting of data consistency and regularization terms) is convex. Therefore, sparsity is more strongly promoted than in the standard convex formulation, but without sacrificing the attractive aspects of convex optimization (unique minimum, robust algorithms, etc.). We use this idea to improve the recently developed ‘overlapping group shrinkage’ (OGS) algorithm for the denoising of group-sparse signals. The algorithm is applied to the problem of speech enhancement with favorable results in terms of both SNR and perceptual quality.

Connected through hyperedge HE-1 (induced by source paper "total variation with overlapping group sparsity and lp quasinorm for infrared image deblurring under salt and pepper noise"):
  with members:
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"
    - "image restoration using total variation with overlapping group sparsity"

Connected through hyperedge HE-2 (induced by source paper "an optimization framework with flexible inexact inner iterations for nonconvex and nonsmooth programming"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "optimization with sparsity inducing penalties"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "repetitive transients extraction algorithm for detecting bearing faults"):
  with members:
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "sparsity based correction of exponential artifacts"
    - "sparse signal estimation by maximally sparse convex optimization"

Connected through hyperedge HE-4 (induced by source paper "enhanced low rank matrix approximation"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "convex denoising using non convex tight frame regularization"
    - "a note on patch based low rank minimization for fast image denoising"
    - "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "patch based low rank minimization for image denoising"

Connected through hyperedge HE-5 (induced by source paper "periodic transients separation algorithm for detecting bearing faults"):
  with members:
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "sparsity based correction of exponential artifacts"
    - "sparse signal estimation by maximally sparse convex optimization"

Connected through hyperedge HE-6 (induced by source paper "detection of faults in rotating machinery using periodic time frequency sparsity"):
  with members:
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "sparsity based correction of exponential artifacts"
    - "translation invariant shrinkage thresholding of group sparse signals"

Connected through hyperedge HE-7 (induced by source paper "sparsity based algorithm for detecting faults in rotating machines"):
  with members:
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "sparse signal estimation by maximally sparse convex optimization"

Connected through hyperedge HE-8 (induced by source paper "sparsity based correction of exponential artifacts"):
  with members:
    - "sparse signal estimation by maximally sparse convex optimization"
    - "optimization with sparsity inducing penalties"

Question: Please tell me which class the center node 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)