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 "design and analysis of a greedy pursuit for distributed compressed sensing"):
  Abstract: We consider a distributed compressed sensing scenario where many sensors measure correlated sparse signals and the sensors are connected through a network. Correlation between sparse signals is modeled by a partial common support-set. For such a scenario, the main objective of this paper is to develop a greedy pursuit algorithm. We develop a distributed parallel pursuit (DIPP) algorithm based on exchange of information about estimated support-sets at sensors. The exchange of information helps to improve estimation of the partial common support-set, that in turn helps to gradually improve estimation of support-sets in all sensors, leading to a better quality reconstruction performance. We provide restricted isometry property (RIP) based theoretical analysis on the algorithm's convergence and reconstruction performance. Under certain theoretical requirements on the quality of information exchange over network and RIP parameters of sensor nodes, we show that the DIPP algorithm converges to a performance level that depends on a scaled additive measurement noise power (convergence in theory) where the scaling coefficient is a function of RIP parameters and information processing quality parameters. Using simulations, we show practical reconstruction performance of DIPP vis-a-vis amount of undersampling, signal-to-measurement-noise ratios and network-connectivity conditions.

Includes members:
  Member "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ssfn self size estimating feed forward network with low complexity limited need for human intervention and consistent behaviour across trials")
  Member "greedy sparsity promoting algorithms for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic and randomized diffusion based iterative generalized hard thresholding difight for distributed sparse signal recovery")
  Member "analysis of democratic voting principles used in distributed greedy algorithms":
    also connected through other hyperedges:
      (none)
  Member "distributed compressed sensing for static and time varying networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "serial quantization for sparse time sequences")
  Member "distributed greedy pursuit algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural network based explicit mixture models and expectation maximization based learning")
  Member "cooperative sparsity pattern recovery in distributed networks via distributed omp":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fusion based cooperative support identification for compressive networked sensing")
  Member "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing")
  Member "projection based and look ahead strategies for atom selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis by synthesis based quantization of compressed sensing measurements")

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)