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 "sparse optimization problem with s difference regularization"):
  Abstract: In this paper, a s-difference type regularization for sparse recovery problem is proposed, which is the difference of the normal penalty function R(x) and its corresponding struncated function R (xs). First, we show the equivalent conditions between the L0 constrained problem and the unconstrained s-difference penalty regularized problem. Next, we choose the forward-backward splitting (FBS) method to solve the nonconvex regularizes function and further derive some closed-form solutions for the proximal mapping of the s-difference regularization with some commonly used R(x), which makes the FBS easy and fast. We also show that any cluster point of the sequence generated by the proposed algorithm converges to a stationary point. Numerical experiments demonstrate the efficiency of the proposed s-difference regularization in comparison with some other existing penalty functions.

Includes members:
  Member "iterative ell_1 minimization for non convex compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing")
  Member "compressed sensing recovery via nonconvex shrinkage penalties":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix completion via nonconvex regularization convergence of the proximal gradient algorithm")
  Member "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "image reconstruction from few views by l0 norm optimization":
    also connected through other hyperedges:
      (none)
  Member "minimization of transformed l_1 penalty theory difference of convex function algorithm and robust application in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a study on graph structured recurrent neural networks and sparsification with application to epidemic forecasting")
  Member "decreasing weighted sorted ell_1 regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotics and optimal designs of slope for sparse linear regression")
  Member "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "binary matrix completion with nonconvex regularizers")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)