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 "improved sufficient conditions for sparse recovery with generalized orthogonal matching pursuit"):
  Abstract: The generalized orthogonal matching pursuit (gOMP), also called the orthogonal multi-matching pursuit (OMMP), is a natural generation of the well-known orthogonal matching pursuit (OMP) in the sense that multiple $N (N\geq1)$ indices are identified per iteration. Sufficient conditions for sparse recovery with OMP and gOMP under restricted isometry property of a sensing matrix have received much attention in recent years. In this paper, we show that if the restricted isometry constant $\delta_{NK+1}$ of the sensing matrix $\A$ satisfies $\delta_{NK+1} < 1/\sqrt {K/N+1} $, then under some conditions on the signal-to-noise ratio (SNR), gOMP recovers at least one index in the support of the $K$-sparse signal $\x$ from an observation vector $\y=\A\x+\v$ (where $\v$ is a noise vector) in each iteration. Surprisingly, this condition do not require $N\leq K$ which is needed in Wang, \textit{et al} 2012 and Liu, \textit{et al} 2012. Thus, $N$ can have more choices. When $N=1$, this sufficient condition turns to be a sufficient condition for support recovery with OMP. We show that it is weaker than that in Wang 2015 in terms of both SNR and RIP. Moreover, in the noise free case, we obtain that $\delta_{NK+1} < 1/\sqrt {K/N+1} $ is a sufficient condition for recovering the $K$-sparse signal $\x$ with gOMP in $K$ iterations which is better than the best known one in terms of $\delta_{NK+1}$. In particular, this condition is sharp when $N=1$.

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 "support recovery with orthogonal matching pursuit in the presence of noise":
    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 "sharp conditions for exact support recovery of sparse signals with noise via omp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit")
  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 "when does omp achieve support recovery with continuous dictionaries")
  Member "improving the bound on the rip constant in generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a sharp bound on ric in generalized orthogonal matching pursuit")
  Member "generalized orthogonal matching pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random access for massive machine type communications")

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)