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 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 $N=1$.

Connected through hyperedge HE-1 (induced by source paper "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"):
  with members:
    - "a sharp bound on ric in generalized orthogonal matching pursuit"
    - "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "generalized orthogonal matching pursuit"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "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.IT(Information Theory)