human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "when does omp achieve support recovery with continuous dictionaries"):
  Abstract: This paper presents new theoretical results on sparse recovery guarantees for a greedy algorithm, Orthogonal Matching Pursuit (OMP), in the context of continuous parametric dictionaries. Here, the continuous setting means that the dictionary is made up of an infinite (potentially uncountable) number of atoms. In this work, we rely on the Hilbert structure of the observation space to express our recovery results as a property of the kernel defined by the inner product between two atoms. Using a continuous extension of Tropp's Exact Recovery Condition, we identify two key notions of admissible kernel and admissible support that are sufficient to ensure exact recovery with OMP. We exhibit a family of admissible kernels relying on completely monotone functions for which admissibility holds for any support in the one-dimensional setting. For higher dimensional parameter spaces, an additional notion of axis admissibility is shown to be sufficient to ensure a form of "delayed" recovery. An additional algebraic condition involving a finite subset of (known) atoms further yields exact recovery guarantees. Finally, a coherence-based viewpoint on these results provides recovery guarantees in terms of a minimum separation assumption.

Includes members:
  Member "a sharp restricted isometry constant bound of orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint sparse recovery using signal space matching pursuit")
  Member "super resolution of positive sources the discrete setup":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "robust recovery of positive stream of pulses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fourier phase retrieval uniqueness and algorithms")
  Member "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fusion based cooperative support identification for compressive networked sensing")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "towards a mathematical theory of super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "improved rip analysis of orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved analysis for subspace pursuit algorithm in terms of restricted isometry constant")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

Question: Please tell me which class the source paper 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)