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: "translation invariant shrinkage thresholding of group sparse signals"
  Abstract: This paper addresses signal denoising when large-amplitude coefficients form clusters (groups). The L1-norm and other separable sparsity models do not capture the tendency of coefficients to cluster (group sparsity). This work develops an algorithm, called 'overlapping group shrinkage' (OGS), based on the minimization of a convex cost function involving a group-sparsity promoting penalty function. The groups are fully overlapping so the denoising method is translation-invariant and blocking artifacts are avoided. Based on the principle of majorization-minimization (MM), we derive a simple iterative minimization algorithm that reduces the cost function monotonically. A procedure for setting the regularization parameter, based on attenuating the noise to a specified level, is also described. The proposed approach is illustrated on speech enhancement, wherein the OGS approach is applied in the short-time Fourier transform (STFT) domain. The OGS algorithm produces denoised speech that is relatively free of musical noise.

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:
    - "group sparse signal denoising non convex regularization convex optimization"
    - "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 "a penalty function promoting sparsity within and across groups"):
  with members:
    - "enhanced low rank matrix approximation"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (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"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-4 (induced by source paper "sparsity based algorithm for detecting faults in rotating machines"):
  with members:
    - "group sparse signal denoising non convex regularization convex optimization"
    - "sparse signal estimation by maximally sparse convex optimization"

Connected through hyperedge HE-5 (induced by source paper "fault detection of rotation machinery using periodic time frequency sparsity"):
  with members:
    - "sparsity based correction of exponential artifacts"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-6 (induced by source paper "fast iteratively reweighted least squares algorithms for analysis based sparsity reconstruction"):
  with members:
    - "group sparse signal denoising non convex regularization convex optimization"
    - "efficient variational inference in large scale bayesian compressed sensing"
    - "optimization with sparsity inducing penalties"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "model based compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "group sparse signal denoising non convex regularization convex optimization"):
  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)