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 "fusion based cooperative support identification for compressive networked sensing"):
  Abstract: This paper proposes a fusion-based cooperative support identification scheme for distributed compressive sparse signal recovery via resource-constrained wireless sensor networks. The proposed support identification protocol involves: (i) local sparse sensing for economizing data gathering and storage, (ii) local binary decision making for partial support knowledge inference, (iii) binary information exchange among active nodes, and (iv) binary data aggregation for support estimation. Then, with the aid of the estimated signal support, a refined local decision is made at each node. Only the measurements of those informative nodes will be sent to the fusion center, which employs a weighted $\ell_1$-minimization for global signal reconstruction. The design of a Bayesian local decision rule is discussed, and the average communication cost is analyzed. Computer simulations are used to illustrate the effectiveness of the proposed scheme.

Includes members:
  Member "wireless compressive sensing over fading channels with distributed sparse random projections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on analog gradient descent learning over multiple access fading channels")
  Member "compressed sensing for wireless communications useful tips and tricks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems")
  Member "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "double detector for sparse signal detection from one bit compressed sensing measurements")
  Member "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when does omp achieve support recovery with continuous dictionaries")
  Member "cooperative sparsity pattern recovery in distributed networks via distributed omp":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "decentralized joint sparse signal recovery a sparse bayesian learning approach")
  Member "collaborative spectrum sensing from sparse observations in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "recovering compressively sampled signals using partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting prior information in block sparse signals")
  Member "bayesian compressive sensing via belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing")

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)