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 approximation via penalty decomposition methods"
  Abstract: In this paper we consider sparse approximation problems, that is, general $l_0$ minimization problems with the $l_0$-"norm" of a vector being a part of constraints or objective function. In particular, we first study the first-order optimality conditions for these problems. We then propose penalty decomposition (PD) methods for solving them in which a sequence of penalty subproblems are solved by a block coordinate descent (BCD) method. Under some suitable assumptions, we establish that any accumulation point of the sequence generated by the PD methods satisfies the first-order optimality conditions of the problems. Furthermore, for the problems in which the $l_0$ part is the only nonconvex part, we show that such an accumulation point is a local minimizer of the problems. In addition, we show that any accumulation point of the sequence generated by the BCD method is a saddle point of the penalty subproblem. Moreover, for the problems in which the $l_0$ part is the only nonconvex part, we establish that such an accumulation point is a local minimizer of the penalty subproblem. Finally, we test the performance of our PD methods by applying them to sparse logistic regression, sparse inverse covariance selection, and compressed sensing problems. The computational results demonstrate that our methods generally outperform the existing methods in terms of solution quality and/or speed.

Connected through hyperedge HE-1 (induced by source paper "120001 0 regularized structured sparsity convolutional neural networks"):
  with members:
    - "transformed ell_1 regularization for learning sparse deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "discrimination aware channel pruning for deep neural networks"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "learning structured sparsity in deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "spectral efficiency optimization for millimeter wave multiuser mimo systems"):
  with members:
    - "hybrid beamforming via the kronecker decomposition for the millimeter wave massive mimo systems"
    - "penalty dual decomposition method for nonsmooth nonconvex optimization"
    - "joint source relay design for full duplex mimo af relay systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "near optimal hybrid processing for massive mimo systems via matrix decomposition"
    - "hybrid block diagonalization for massive multiuser mimo systems"

Connected through hyperedge HE-3 (induced by source paper "ell_0 tv a sparse optimization method for impulse noise image restoration"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"

Connected through hyperedge HE-4 (induced by source paper "a coordinate wise optimization algorithm for sparse inverse covariance selection"):
  with members:
    - "convex optimization for linear query processing under approximate differential privacy"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "projected subgradient methods for learning sparse gaussians"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-5 (induced by source paper "joint source relay design for full duplex mimo af relay systems"):
  with members:
    - "joint beamforming optimization and power control for full duplex mimo two way relay channel"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "low complexity end to end performance optimization in mimo full duplex relay systems"

Connected through hyperedge HE-6 (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"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "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.LG(Machine Learning)