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 "nonconvex and nonsmooth sparse optimization via adaptively iterative reweighted methods"):
  Abstract: We present a general formulation of nonconvex and nonsmooth sparse optimization problems with a convexset constraint, which takes into account most existing types of nonconvex sparsity-inducing terms. It thus brings strong applicability to a wide range of applications. We further design a general algorithmic framework of adaptively iterative reweighted algorithms for solving the nonconvex and nonsmooth sparse optimization problems. This is achieved by solving a sequence of weighted convex penalty subproblems with adaptively updated weights. The first-order optimality condition is then derived and the global convergence results are provided under loose assumptions. This makes our theoretical results a practical tool for analyzing a family of various iteratively reweighted algorithms. In particular, for the iteratively reweighed $\ell_1$-algorithm, global convergence analysis is provided for cases with diminishing relaxation parameter. For the iteratively reweighed $\ell_2$-algorithm, adaptively decreasing relaxation parameter is applicable and the existence of the cluster point to the algorithm is established. The effectiveness and efficiency of our proposed formulation and the algorithms are demonstrated in numerical experiments in various sparse optimization problems.

Includes members:
  Member "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference")
  Member "generalized sparse and low rank optimization for ultra dense networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coordinated passive beamforming for distributed intelligent reflecting surfaces network")
  Member "content centric sparse multicast beamforming for cache enabled cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint optimization of file placement and delivery in cache assisted wireless networks with limited lifetime and cache space")
  Member "iterative concave rank approximation for recovering low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "proximal iteratively reweighted algorithm with multiple splitting for nonconvex sparsity optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse optimization for green edge ai inference")
  Member "group sparse beamforming for green cloud ran":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse optimization for green edge ai inference")
  Member "optimization with sparsity inducing penalties":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse optimization for green edge ai inference")

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)