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 "an information theoretic study for noisy compressed sensing with joint sparsity model 2"):
  Abstract: In this paper, we study a support set reconstruction problem in which the signals of interest are jointly sparse with a common support set, and sampled by joint sparsity model-2 (JSM-2) in the presence of noise. Using mathematical tools, we develop upper and lower bounds on the failure probability of support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal to noise ratio, the number of measurement vectors and the number of measurements. These bounds can be used to provide a guideline to determine the system parameters in various applications of compressed sensing with noisy JSM-2. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to give theoretical explanations about the benefits enabled by joint sparsity structure in noisy JSM-2. We compare our sufficient condition with the existing result of noisy multiple measurement vectors model (MMV). As a result, we show that noisy JSM-2 may require less number of measurements than noisy MMV for reliable support set reconstruction.

Includes members:
  Member "limits on support recovery with probabilistic models an information theoretic framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group testing an information theory perspective")
  Member "subspace recovery from structured union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "omp based joint sparsity pattern recovery under communication constraints")
  Member "measurement bounds for sparse signal ensembles via graphical models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance limits with additive error metrics in noisy multimeasurement vector problems")
  Member "support recovery of sparse signals in the presence of multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint sparse recovery using signal space matching pursuit")
  Member "information theoretic bounds for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "random access for massive machine type communications")
  Member "distributed compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "serial quantization for sparse time sequences")
  Member "performance analysis for sparse support recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")
  Member "necessary and sufficient conditions for sparsity pattern recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "harmless interpolation of noisy data in regression")

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)