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: "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
  Abstract: In recent studies on sparse modeling, the nonconvex regularization approaches (particularly, Lq regularization with q ∈ (0,1)) have been demonstrated to possess capability of gaining much benefit in sparsity-inducing and efficiency. As compared with the convex regularization approaches (say, L1 regularization), however, the convergence issue of the corresponding algorithms are more difficult to tackle. In this paper, we deal with this difficult issue for a specific but typical nonconvex regularization scheme, the L1/2 regularization, which has been successfully used to many applications. More specifically, we study the convergence of the iterative half thresholding algorithm (the half algorithm for short), one of the most efficient and important algorithms for solution to the L1/2 regularization. As the main result, we show that under certain conditions, the half algorithm converges to a local minimizer of the L1/2 regularization, with an eventually linear convergence rate. The established result provides a theoretical guarantee for a wide range of applications of the half algorithm. We provide also a set of simulations to support the correctness of theoretical assertions and compare the time efficiency of the half algorithm with other known typical algorithms forL1/2 regularization like the iteratively reweighted least squares (IRLS) algorithm and the iteratively reweighted l1 minimization (IRL1) algorithm.

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"
    - "compressed sensing recovery via nonconvex shrinkage penalties"
    - "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 "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"
    - "group sparse signal denoising non convex regularization convex optimization"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"
    - "image restoration using total variation with overlapping group sparsity"

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 "tractable and scalable schatten quasi norm approximations for rank minimization"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "learning optimal nonlinearities for iterative thresholding algorithms"):
  with members:
    - "a deep learning approach to structured signal recovery"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "learning activation functions to improve deep neural networks"
    - "learning efficient structured sparse models"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-6 (induced by source paper "a survey of sparse representation algorithms and applications"):
  with members:
    - "group based sparse representation for image restoration"
    - "constructing a non negative low rank and sparse graph with data adaptive features"
    - "sparse recovery of streaming signals using l1 homotopy"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "an mdl framework for sparse coding and dictionary learning"
    - "image super resolution via dual dictionary learning and sparse representation"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "fast l1 minimization algorithms for robust face recognition"

Connected through hyperedge HE-7 (induced by source paper "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"):
  with members:
    - "iterative ell_1 minimization for non convex compressed sensing"
    - "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
    - "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms"
    - "super resolution from noisy data"
    - "sparse approximation via penalty decomposition methods"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-8 (induced by source paper "minimization of transformed l_1 penalty closed form representation and iterative thresholding algorithms"):
  with members:
    - "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing"
    - "super resolution from noisy data"
    - "coherence pattern guided compressive sensing with unresolved grids"

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.NA(Numerical Analysis)