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 "linear convergence and support recovery for non convex multi penalty regularization"):
  Abstract: We provide a comprehensive convergence study of the iterative multi-penalty $q-$thresholding algorithm, with $0<q\leq 1$, for recovery of a signal mixture. Leveraging recent results in optimisation, signal processing, and regularization, we present novel results on linear convergence of iterates to local minimizers for studied non-convex multi-penalty functionals. We also provide explicitly compute the convergence constant and establish its dependence with respect to the measurement matrix and parameters of the problem. Finally, we present extensive numerical results, that confirm the theoretical findings, and compare the efficiency of the iterative multi-penalty thresholding algorithm with single-penalty counterpart.

Includes members:
  Member "tradeoffs between convergence speed and reconstruction accuracy in inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "noise folding in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "random access for massive machine type communications")

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)