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: "support recovery with orthogonal matching pursuit in the presence of noise"
  Abstract: Support recovery of sparse signals from compressed linear measurements is a fundamental problem in compressed sensing (CS). In this article, we study the orthogonal matching pursuit (OMP) algorithm for the recovery of support under noise. We consider two signal-to-noise ratio (SNR) settings: 1) the SNR depends on the sparsity level   $K$    of input signals, and 2) the SNR is an absolute constant independent of   $K$  . For the first setting, we establish necessary and sufficient conditions for the exact support recovery with OMP, expressed as lower bounds on the SNR. Our results indicate that in order to ensure the exact support recovery of all   $K$  -sparse signals with the OMP algorithm, the SNR must at least scale linearly with the sparsity level   $K$  . In the second setting, since the necessary condition on the SNR is not fulfilled, the exact support recovery with OMP is impossible. However, our analysis shows that recovery with an arbitrarily small but constant fraction of errors is possible with the OMP algorithm. This result may be useful for some practical applications where obtaining some large fraction of support positions is adequate.

Connected through hyperedge HE-1 (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"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "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-2 (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"
    - "sharp conditions for exact support recovery of sparse signals with noise via omp"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"

Connected through hyperedge HE-3 (induced by source paper "mobility aware uplink interference model for 5g heterogeneous networks"):
  with members:
    - "an overview of load balancing in hetnets old myths and open problems"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"
    - "user association for load balancing in heterogeneous cellular networks"
    - "modeling heterogeneous network interference using poisson point processes"

Connected through hyperedge HE-4 (induced by source paper "a novel sufficient condition for generalized orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"

Connected through hyperedge HE-5 (induced by source paper "sharp conditions for exact support recovery of sparse signals with noise via omp"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
    - "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"
    - "new bounds for restricted isometry constants"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "recovery of sparse signals via generalized orthogonal matching pursuit a new analysis"):
  with members:
    - "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"
    - "near optimal bound of orthogonal matching pursuit using restricted isometric constant"
    - "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"

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)