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 "decentralized and collaborative subspace pursuit a communication efficient algorithm for joint sparsity pattern recovery"):
  Abstract: In this paper, we consider the problem of joint sparsity pattern recovery in a decentralized network and propose an algorithm named decentralized and collaborative subspace pursuit (DCSP). The basic idea of DCSP is to embed collaboration among nodes into each iteration of the standard subspace pursuit (SP) algorithm. At each iteration of the DCSP algorithm, local estimation of the support set is carried out at every node by finding the subspace that the local measurement vector most probably lies in, and then the global estimate of the support set is obtained by fusion of all the local estimates. An attractive characteristic of DCSP is the small number of messages to be transmitted among nodes, which is helpful when the communication capacity of the network is limited. Compared to existing decentralized greedy algorithms, DCSP offers satisfactory accuracy of sparsity pattern recovery with much less communication cost. We further extend DCSP to the generalized DCSP (GDCSP) algorithm, by allowing each node to share more local information with its surrounding neighbors. GDCSP outperforms DCSP in terms of the accuracy of sparsity pattern recovery at the cost of slightly increased communication overhead.

Includes members:
  Member "decentralized subspace pursuit for joint sparsity pattern recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized joint sparse signal recovery a sparse bayesian learning approach")
  Member "cooperative sparsity pattern recovery in distributed networks via distributed omp":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fusion based cooperative support identification for compressive networked sensing")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)