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: "improved rip analysis of orthogonal matching pursuit"
  Abstract: Orthogonal Matching Pursuit (OMP) has long been considered a powerful heuristic for attacking compressive sensing problems; however, its theoretical development is, unfortunately, somewhat lacking. This paper presents an improved Restricted Isometry Property (RIP) based performance guarantee for T-sparse signal reconstruction that asymptotically approaches the conjectured lower bound given in Davenport et al. We also further extend the state-of-the-art by deriving reconstruction error bounds for the case of general non-sparse signals subjected to measurement noise. We then generalize our results to the case of K-fold Orthogonal Matching Pursuit (KOMP). We finish by presenting an empirical analysis suggesting that OMP and KOMP outperform other compressive sensing algorithms in average case scenarios. This turns out to be quite surprising since RIP analysis (i.e. worst case scenario) suggests that these matching pursuits should perform roughly T^0.5 times worse than convex optimization, CoSAMP, and Iterative Thresholding.

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"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "towards a mathematical theory of super resolution"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "learning gaussian mixtures with arbitrary separation"

Connected through hyperedge HE-2 (induced by source paper "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant"):
  with members:
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "oblique pursuits for compressed sensing"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "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"
    - "near optimal bound of orthogonal matching pursuit using restricted isometric constant"
    - "generalized 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 "exact recovery conditions for sparse representations with partial support information"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "sparse signal reconstruction via iterative support detection"
    - "recovering compressively sampled signals using partial support information"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "a short note on compressed sensing with partially known signal support"
    - "weighted ell_1 minimization for sparse recovery with prior information"

Connected through hyperedge HE-5 (induced by source paper "achievable angles between two compressed sparse vectors under norm distance constraints imposed by the restricted isometry property a plane geometry approach"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-6 (induced by source paper "greedy type algorithms for rip matrices a study of two selection rules"):
  with members:
    - "remarks on the restricted isometry property in orthogonal matching pursuit algorithm"

Connected through hyperedge HE-7 (induced by source paper "the performance of orthogonal multi matching pursuit under rip"):
  with members:
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "new bounds for restricted isometry constants"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (induced by source paper "generalized orthogonal matching pursuit"):
  with members:
    - "near optimal bound of orthogonal matching pursuit using restricted isometric constant"
    - "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.DS(Data Structures and Algorithms)