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: "support recovery of sparse signals"
  Abstract: We consider the problem of exact support recovery of sparse signals via noisy measurements. The main focus is the sufficient and necessary conditions on the number of measurements for support recovery to be reliable. By drawing an analogy between the problem of support recovery and the problem of channel coding over the Gaussian multiple access channel, and exploiting mathematical tools developed for the latter problem, we obtain an information theoretic framework for analyzing the performance limits of support recovery. Sharp sufficient and necessary conditions on the number of measurements in terms of the signal sparsity level and the measurement noise level are derived. Specifically, when the number of nonzero entries is held fixed, the exact asymptotics on the number of measurements for support recovery is developed. When the number of nonzero entries increases in certain manners, we obtain sufficient conditions tighter than existing results. In addition, we show that the proposed methodology can deal with a variety of models of sparse signal recovery, hence demonstrating its potential as an effective analytical tool.

Connected through hyperedge HE-1 (induced by source paper "cooperative sparsity pattern recovery in distributed networks via distributed omp"):
  with members:
    - "performance analysis for sparse support recovery"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-2 (induced by source paper "reduced dimension multiuser detection"):
  with members:
    - "asynchronous code division random access using convex optimization"
    - "structured compressed sensing from theory to applications"
    - "large system analysis of multiuser detection with an unknown number of users a high snr approach"
    - "noise folding in compressed sensing"
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "performance analysis for sparse support recovery"

Connected through hyperedge HE-3 (induced by source paper "support recovery of sparse signals in the presence of multiple measurement vectors"):
  with members:
    - "gps signal acquisition via compressive multichannel sampling"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "shannon theoretic limits on noisy compressive sampling"

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)