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: "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
  Abstract: A sufficient condition reported very recently for perfect recovery of a K-sparse vector via orthogonal matching pursuit (OMP) in K iterations is that the restricted isometry constant of the sensing matrix satisfies delta_K+1<1/(sqrt(delta_K+1)+1). By exploiting an approximate orthogonality condition characterized via the achievable angles between two orthogonal sparse vectors upon compression, this paper shows that the upper bound on delta can be further relaxed to delta_K+1<(sqrt(1+4*delta_K+1)-1)/(2K).This result thus narrows the gap between the so far best known bound and the ultimate performance guarantee delta_K+1<1/(sqrt(delta_K+1)) that is conjectured by Dai and Milenkovic in 2009. The proposed approximate orthogonality condition is also exploited to derive less restricted sufficient conditions for signal reconstruction in several compressive sensing problems, including signal recovery via OMP in a noisy environment, compressive domain interference cancellation, and support identification via the subspace pursuit algorithm.

Connected through hyperedge HE-1 (induced by source paper "when does omp achieve support recovery with continuous dictionaries"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "super resolution of positive sources the discrete setup"
    - "robust recovery of positive stream of pulses"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "towards a mathematical theory of super resolution"
    - "improved rip analysis of orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "learning gaussian mixtures with arbitrary separation"

Connected through hyperedge HE-2 (induced by source paper "fusion based cooperative support identification for compressive networked sensing"):
  with members:
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "recovering compressively sampled signals using partial support information"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (induced by source paper "an optimal restricted isometry condition for exact sparse recovery with orthogonal least squares"):
  with members:
    - "nearly optimal bounds for orthogonal least squares"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "support recovery with orthogonal matching pursuit in the presence of noise"
    - "exact recovery conditions for sparse representations with partial support information"
    - "rank awareness in joint sparse recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "a sharp bound on ric in generalized orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "generalized orthogonal matching pursuit"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-5 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"):
  with members:
    - "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "perturbation analysis of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "nearly optimal bounds for orthogonal least squares"):
  with members:
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "perturbation analysis of 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"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "block compressed sensing based distributed device detection for m2m communications"):
  with members:
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "newton like method with diagonal correction for distributed optimization"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "condition numbers of gaussian random matrices"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "improved sufficient conditions for sparse recovery with generalized orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "support recovery with orthogonal matching pursuit in the presence of noise"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"

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)