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: "near optimal bound of orthogonal matching pursuit using restricted isometric constant"
  Abstract: As a paradigm for reconstructing sparse signals using a set of under sampled measurements, compressed sensing has received much attention in recent years. In identifying the sufficient condition under which the perfect recovery of sparse signals is ensured, a property of the sensing matrix referred to as the restricted isometry property (RIP) is popularly employed. In this article, we propose the RIP based bound of the orthogonal matching pursuit (OMP) algorithm guaranteeing the exact reconstruction of sparse signals. Our proof is built on an observation that the general step of the OMP process is in essence the same as the initial step in the sense that the residual is considered as a new measurement preserving the sparsity level of an input vector. Our main conclusion is that if the restricted isometry constant δ#N#                  K#N#                of the sensing matrix satisfies #N#                #N#                  #N#                    #N#                  #N#                  #N#                    #N#                  #N#                #N#               then the OMP algorithm can perfectly recover K(> 1)-sparse signals from measurements. We show that our bound is sharp and indeed close to the limit conjectured by Dai and Milenkovic.

Connected through hyperedge HE-1 (induced by source paper "on the exact recovery condition of simultaneous orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "simultaneous orthogonal matching pursuit with noise stabilization theoretical analysis"
    - "coherence and rip analysis for greedy algorithms in compressive sensing"
    - "orthogonal matching pursuit with thresholding and its application in compressive sensing"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-2 (induced by source paper "support recovery with orthogonal matching pursuit in the presence of noise"):
  with members:
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"
    - "orthogonal matching pursuit a brownian motion analysis"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "recovery of sparse signals via generalized orthogonal matching pursuit a new analysis"):
  with members:
    - "support recovery with orthogonal matching pursuit in the presence of noise"
    - "high speed railway wireless communications efficiency versus fairness"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "perturbation analysis of orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-4 (induced by source paper "generalized orthogonal matching pursuit"):
  with members:
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "compressed sensing performance bounds under poisson noise"
    - "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)