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: "improving a omp"
  Abstract: Best-first search has been recently utilized for compressed sensing (CS) by the A ? orthogonal matching pursuit ( A ? OMP ) algorithm. In this work, we concentrate on theoretical and empirical analyses of A ? OMP . We present a restricted isometry property (RIP) based general condition for exact recovery of sparse signals via A ? OMP . In addition, we develop online guarantees which promise improved recovery performance with the residue-based termination instead of the sparsity-based one. We demonstrate the recovery capabilities of A ? OMP with extensive recovery simulations using the adaptive-multiplicative (AMul) cost model, which effectively compensates for the path length differences in the search tree. The presented results, involving phase transitions for different nonzero element distributions as well as recovery rates and average error, reveal not only the superior recovery accuracy of A ? OMP , but also the improvements with the residue-based termination and the AMul cost model. Comparison of the run times indicates the speed up by the AMul cost model. We also demonstrate a hybrid of OMP and A ? OMP to accelerate the search further. Finally, we run A ? OMP on sparse images to illustrate its recovery performance for more realistic coefficient distributions. HighlightsWe analyze the empirical and theoretical performance of A ? OMP in sparse recovery.RIP-based theoretical analysis provides recovery guarantees for A ? OMP .The introduced novel dynamic cost model improves recovery performance significantly.Results of computationally expensive experiments are presented by phase transitions. A ? OMP outperforms conventional methods in a wide range of sparse recovery scenarios.

Connected through hyperedge HE-1 (induced by source paper "analysis of a low memory implementation of the orthogonal matching pursuit greedy strategy"):
  with members:
    - "a dedicated greedy pursuit algorithm for sparse spectral representation of music sound"
    - "cooperative greedy pursuit strategies for sparse signal representation by partitioning"
    - "hierarchized block wise image approximation by greedy pursuit strategies"

Connected through hyperedge HE-2 (induced by source paper "comments on multipath matching pursuit by kwon wang and shim"):
  with members:
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"

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)