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 "relaxed recovery conditions for omp ols by exploiting both coherence and decay"):
  Abstract: We propose extended coherence-based conditions for exact sparse support recovery using orthogonal matching pursuit (OMP) and orthogonal least squares (OLS). Unlike standard uniform guarantees, we embed some information about the decay of the sparse vector coefficients in our conditions. As a result, the standard condition $\mu<1/(2k-1)$ (where $\mu$ denotes the mutual coherence and $k$ the sparsity level) can be weakened as soon as the non-zero coefficients obey some decay, both in the noiseless and the bounded-noise scenarios. Furthermore, the resulting condition is approaching $\mu<1/k$ for strongly decaying sparse signals. Finally, in the noiseless setting, we prove that the proposed conditions, in particular the bound $\mu<1/k$, are the tightest achievable guarantees based on mutual coherence.

Includes members:
  Member "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when does omp achieve support recovery with continuous dictionaries")
  Member "exact recovery conditions for sparse representations with partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an optimal restricted isometry condition for exact sparse recovery with orthogonal least squares")
  Member "a remark on the restricted isometry property in orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection")
  Member "sparse vector distributions and recovery from compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal binary compressed sensing matrix")
  Member "sparse recovery with orthogonal matching pursuit under rip":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "optimally tuned iterative reconstruction algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random access for massive machine type communications")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "average case analysis of multichannel sparse recovery using convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sample measurement tradeoff in support recovery under a subgaussian prior")

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)