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: "perturbation analysis of orthogonal matching pursuit"
  Abstract: Orthogonal Matching Pursuit (OMP) is a canonical greedy pursuit algorithm for sparse approximation. Previous studies of OMP have considered the recovery of a sparse signal through Φ and y = Φx + b, where is a matrix with more columns than rows and denotes the measurement noise. In this paper, based on Restricted Isometry Property (RIP), the performance of OMP is analyzed under general perturbations, which means both y and Φ are perturbed. Though the exact recovery of an almost sparse signal x is no longer feasible, the main contribution reveals that the support set of the best k-term approximation of x can be recovered under reasonable conditions. The error bound between x and the estimation of OMP is also derived. By constructing an example it is also demonstrated that the sufficient conditions for support recovery of the best k-term approximation of are rather tight. When x is strong-decaying, it is proved that the sufficient conditions for support recovery of the best k-term approximation of x can be relaxed, and the support can even be recovered in the order of the entries' magnitude. Our results are also compared in detail with some related previous ones.

Connected through hyperedge HE-1 (induced by source paper "preconditioned multiple orthogonal least squares and applications in ghost imaging via sparsity constraint"):
  with members:
    - "nearly optimal bounds for orthogonal least squares"
    - "generalized orthogonal matching pursuit"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (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"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "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"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"

Connected through hyperedge HE-3 (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"
    - "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"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "cloud k svd a collaborative dictionary learning algorithm for big distributed data"):
  with members:
    - "dictionary learning over distributed models"
    - "multitask diffusion adaptation over networks"
    - "generalized principal component analysis gpca"
    - "hybrid linear modeling via local best fit flats"
    - "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks"
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"

Connected through hyperedge HE-5 (induced by source paper "noise folding based on general complete perturbation in compressed sensing"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "noise folding in compressed sensing"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "on the performance bound of sparse estimation with sensing matrix perturbation"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "noise folding in compressed sensing"
    - "how well can we estimate a sparse vector"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"

Connected through hyperedge HE-8 (induced by source paper "oracle order recovery performance of greedy pursuits with replacement against general perturbations"):
  with members:
    - "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressed sensing performance bounds under poisson noise"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "new bounds for restricted isometry constants"

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)