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: "convex denoising using non convex tight frame regularization"
  Abstract: This letter considers the problem of signal denoising using a sparse tight-frame analysis prior. The    ${\ell _1}$   norm has been extensively used as a regularizer to promote sparsity; however, it tends to under-estimate non-zero values of the underlying signal. To more accurately estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to ensure convexity of the objective function. The convexity of the objective function is ensured by constraining the parameter of the non-convex penalty. We use ADMM to obtain a solution and show how to guarantee that ADMM converges to the global optimum of the objective function. We illustrate the proposed method for 1D and 2D signal denoising.

Connected through hyperedge HE-1 (induced by source paper "multi focus image fusion using sparse representation and coupled dictionary learning"):
  with members:
    - "image fusion with cosparse analysis operator"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-2 (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"
    - "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"
    - "group sparse signal denoising non convex regularization convex optimization"

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)