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: "distributed greedy pursuit algorithms"
  Abstract: For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes. Based on this signal model along with a brief survey of existing greedy algorithms, we develop distributed greedy algorithms with low communication overhead. Incorporating appropriate modifications, we design two new distributed algorithms where the local algorithms are based on appropriately modified existing orthogonal matching pursuit and subspace pursuit. Further, by combining advantages of these two local algorithms, we design a new greedy algorithm that is well suited for a distributed scenario. By extensive simulations we demonstrate that the new algorithms in a sparsely connected network provide good performance, close to the performance of a centralized greedy solution.

Connected through hyperedge HE-1 (induced by source paper "neural network based explicit mixture models and expectation maximization based learning"):
  with members:
    - "improved training of generative adversarial networks using representative features"
    - "disconnected manifold learning for generative adversarial networks"
    - "improving gans using optimal transport"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "boosted generative models"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "improving a omp"):
  with members:
    - "compressed sensing signal recovery via forward backward pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "sparse signal reconstruction via iterative support detection"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "decentralized and collaborative subspace pursuit a communication efficient algorithm for joint sparsity pattern recovery with sensor networks"):
  with members:
    - "omp based joint sparsity pattern recovery under communication constraints"
    - "distributed compressed sensing for static and time varying networks"
    - "decentralized subspace pursuit for joint sparsity pattern recovery"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "design and analysis of a greedy pursuit for distributed compressed sensing"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "analysis of democratic voting principles used in distributed greedy algorithms"
    - "distributed compressed sensing for static and time varying networks"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
    - "projection based and look ahead strategies for atom selection"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "omp based joint sparsity pattern recovery under communication constraints"):
  with members:
    - "subspace recovery from structured union of subspaces"
    - "applications of compressed sensing in communications networks"
    - "theory and applications of compressed sensing"
    - "distributed sparse signal recovery for sensor networks"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "analysis of democratic voting principles used in distributed greedy algorithms"):
  with members:
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)