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 "on the conditions of sparse parameter estimation via log sum penalty regularization"):
  Abstract: For high-dimensional sparse parameter estimation problems, Log-Sum Penalty (LSP) regularization effectively reduces the sampling sizes in practice. However, it still lacks theoretical analysis to support the experience from previous empirical study. The analysis of this article shows that, like ‘0-regularization, O(s) sampling size is enough for proper LSP, where s is the non-zero components of the true parameter. We also propose an efficient algorithm to solve LSP regularization problem. The solutions given by the proposed algorithm give consistent parameter estimations under less restrictive conditions than‘1-regularization.

Includes members:
  Member "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "binary matrix completion with nonconvex regularizers")
  Member "complexity of unconstrained l_2 l_p minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "new bounds for restricted isometry constants":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the use of mutual coherence to prove ell 1 ell 0 equivalence in classification problems")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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.IT(Information Theory)