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: "sparse signal estimation by maximally sparse convex optimization"
  Abstract: This paper addresses the problem of sparsity penalized least squares for applications in sparse signal processing, e.g., sparse deconvolution. This paper aims to induce sparsity more strongly than L1 norm regularization, while avoiding non-convex optimization. For this purpose, this paper describes the design and use of non-convex penalty functions (regularizers) constrained so as to ensure the convexity of the total cost function F to be minimized. The method is based on parametric penalty functions, the parameters of which are constrained to ensure convexity of F. It is shown that optimal parameters can be obtained by semidefinite programming (SDP). This maximally sparse convex (MSC) approach yields maximally non-convex sparsity-inducing penalty functions constrained such that the total cost function F is convex. It is demonstrated that iterative MSC (IMSC) can yield solutions substantially more sparse than the standard convex sparsity-inducing approach, i.e., L1 norm minimization.

Connected through hyperedge HE-1 (induced by source paper "feedback acquisition and reconstruction of spectrum sparse signals by predictive level comparisons"):
  with members:
    - "fast methods for recovering sparse parameters in linear low rank models"
    - "iterative methods for sparse signal reconstruction from level crossings"
    - "quantization of compressive samples with stable and robust recovery"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "regime change bit depth versus measurement rate in compressive sensing"

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

Connected through hyperedge HE-3 (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-4 (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"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-5 (induced by source paper "sparsity based algorithm for detecting faults in rotating machines"):
  with members:
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-6 (induced by source paper "sparsity based correction of exponential artifacts"):
  with members:
    - "group sparse signal denoising non convex regularization convex optimization"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-7 (induced by source paper "convex denoising using non convex tight frame regularization"):
  with members:
    - "a penalty function promoting individual sparsity in groups"

Connected through hyperedge HE-8 (induced by source paper "homotopy based algorithms for ell_0 regularized least squares"):
  with members:
    - "projection based and look ahead strategies for atom selection"
    - "universal regularizers for robust sparse coding and modeling"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

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.LG(Machine Learning)