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 "omp based joint sparsity pattern recovery under communication constraints"):
  Abstract: We address the problem of joint sparsity pattern recovery based on low dimensional multiple measurement vectors (MMVs) in resource constrained distributed networks. We assume that distributed nodes observe sparse signals which share the same sparsity pattern and each node obtains measurements via a low dimensional linear operator. When the measurements are collected at distributed nodes in a communication network, it is often required that joint sparse recovery be performed under inherent resource constraints such as communication bandwidth and transmit/processing power. #R##N#We present two approaches to take the communication constraints into account while performing common sparsity pattern recovery. First, we explore the use of a shared multiple access channel (MAC) in forwarding observations residing at each node to a fusion center. With MAC, while the bandwidth requirement does not depend on the number of nodes, the fusion center has access to only a linear combination of the observations. We discuss the conditions under which the common sparsity pattern can be estimated reliably. Second, we develop two collaborative algorithms based on Orthogonal Matching Pursuit (OMP), to jointly estimate the common sparsity pattern in a decentralized manner with a low communication overhead. In the proposed algorithms, each node exploits collaboration among neighboring nodes by sharing a small amount of information for fusion at different stages in estimating the indices of the true support in a greedy manner. Efficiency and effectiveness of the proposed algorithms are demonstrated via simulations along with a comparison with the most related existing algorithms considering the trade-off between the performance gain and the communication overhead.

Includes members:
  Member "distributed greedy pursuit algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural network based explicit mixture models and expectation maximization based learning")
  Member "subspace recovery from structured union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an information theoretic study for noisy compressed sensing with joint sparsity model 2")
  Member "applications of compressed sensing in communications networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "theory and applications of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting prior knowledge in compressed sensing wireless ecg systems")
  Member "distributed sparse signal recovery for sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems")
  Member "block sparse signals uncertainty relations and efficient recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dr2 net deep residual reconstruction network for image compressive sensing")
  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 "average case analysis of multichannel sparse recovery using convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")

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)