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: "compressed sensing recovery via nonconvex shrinkage penalties"
  Abstract: The minimization of compressed sensing is often relaxed to , which yields easy computation using the shrinkage mapping known as soft thresholding, and can be shown to recover the original solution under certain hypotheses. Recent work has derived a general class of shrinkages and associated nonconvex penalties that better approximate the original penalty and empirically can recover the original solution from fewer measurements. We specifically examine p-shrinkage and firm thresholding. In this work, we prove that given data and a measurement matrix from a broad class of matrices, one can choose parameters for these classes of shrinkages to guarantee exact recovery of the sparsest solution. We further prove convergence of the algorithm iterative p-shrinkage (IPS) for solving one such relaxed problem.

Connected through hyperedge HE-1 (induced by source paper "matrix completion via nonconvex regularization convergence of the proximal gradient algorithm"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"

Connected through hyperedge HE-2 (induced by source paper "sparse optimization problem with s difference regularization"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "image reconstruction from few views by l0 norm optimization"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "decreasing weighted sorted ell_1 regularization"
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-4 (induced by source paper "a penalty function promoting sparsity within and across groups"):
  with members:
    - "enhanced low rank matrix approximation"
    - "translation invariant shrinkage thresholding of group sparse signals"
    - "exact matrix completion via 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.IT(Information Theory)