human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "successive convex approximation algorithms for sparse signal estimation with nonconvex regularizations"):
  Abstract: In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization–minimization framework and the successive convex approximation framework proposed in literature for a convex regularization function. The proposed framework has several attractive features, namely, first, flexibility, as different choices of the approximate function lead to different types of algorithms; second, fast convergence, as the problem structure can be better exploited by a proper choice of the approximate function and the stepsize is calculated by the line search; third, low complexity, as the approximate function is convex and the line search scheme is carried out over a differentiable function; fourth, guaranteed convergence to a stationary point. We demonstrate these features by two example applications in subspace learning, namely the network anomaly detection problem and the sparse subspace clustering problem. Customizing the proposed framework by adopting the best-response type approximation, we obtain soft-thresholding with exact line search algorithms for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. The attractive features of the proposed algorithms are illustrated numerically.

Includes members:
  Member "a parallel best response algorithm with exact line search for nonconvex sparsity regularized rank minimization":
    also connected through other hyperedges:
      (none)
  Member "multidimensional sparse recovery for mimo channel parameter estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "block and rank sparse recovery for direction finding in partly calibrated arrays")
  Member "decomposition by partial linearization parallel optimization of multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convergent policy optimization for safe reinforcement learning")
  Member "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "binary matrix completion with nonconvex regularizers")
  Member "dynamic anomalography tracking network anomalies via sparsity and low rank":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient exploration for rl with unsupervised learning")

Question: Please tell me which class the source paper 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)