human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of approximate message passing for distributed compressed sensing"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "performance limits for noisy multimeasurement vector problems"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "decentralized and collaborative subspace pursuit a communication efficient algorithm for joint sparsity pattern recovery with sensor networks"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "decentralized subspace pursuit for joint sparsity pattern recovery"
    - "distributed greedy pursuit algorithms"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "wireless compressive sensing over fading channels with distributed sparse random projections"):
  with members:
    - "efficient compressive sampling of spatially sparse fields in wireless sensor networks"
    - "wireless compressive sensing for energy harvesting sensor nodes"
    - "a probabilistic and ripless theory of compressed sensing"
    - "nonuniform sparse recovery with gaussian matrices"

Connected through hyperedge HE-4 (induced by source paper "enhanced joint sparsity via iterative support detection"):
  with members:
    - "joint sparse recovery with semisupervised music"
    - "multi task sparse structure learning"
    - "exact joint sparse frequency recovery via optimization methods"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "improving noise robustness in subspace based joint sparse recovery"
    - "subspace methods for joint sparse recovery"
    - "joint sparsity with different measurement matrices"
    - "compressive music with optimized partial support for joint sparse recovery"

Question: Please tell me which class the center node 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)