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 "near optimal binary compressed sensing matrix"):
  Abstract: Compressed sensing is a promising technique that attempts to faithfully recover sparse signal with as few linear and nonadaptive measurements as possible. Its performance is largely determined by the characteristic of sensing matrix. Recently several zero-one binary sensing matrices have been deterministically constructed for their relative low complexity and competitive performance. Considering the implementation complexity, it is of great practical interest if one could further improve the sparsity of binary matrix without performance loss. Based on the study of restricted isometry property (RIP), this paper proposes the near-optimal binary sensing matrix, which guarantees nearly the best performance with as sparse distribution as possible. The proposed near-optimal binary matrix can be deterministically constructed with progressive edge-growth (PEG) algorithm. Its performance is confirmed with extensive simulations.

Includes members:
  Member "ldpc codes for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unknown sparsity in compressed sensing denoising and inference")
  Member "sparse vector distributions and recovery from compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relaxed recovery conditions for omp ols by exploiting both coherence and decay")
  Member "sparse reconstruction via the reed muller sieve":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structured sublinear compressive sensing via belief propagation")
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "frame moments and welch bound with erasures")
  Member "deterministic construction of binary bipolar and ternary compressed sensing matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic sparse fourier transform with an ell_infty guarantee")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)