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: "sharp conditions for exact support recovery of sparse signals with noise via omp"
  Abstract: Sufficient conditions for exact support recovery of a sparse signal from noisy measurements with orthogonal matching pursuit (OMP) have been extensively studied in the literature. In this paper, we first show that if the restricted isometry constant (RIC) $\delta_{k+1}(\A)$ of the sensing matrix $\A$ satisfies $\delta_{K+1}(\A) < \frac{1}{\sqrt {K+1} }$, then under some constraints on the minimum magnitude of the nonzero elements of the $K-$sparse signal $\x$, the support of $\x$ can be exactly recovered from the measurements $\y=\A\x+\v$ by OMP under the $l_2$ and $l_{\infty}$ bounded noises. These two conditions are sharp in terms of the RIC since for any given positive integer $K\geq 2$ and for any $\frac{1}{\sqrt{K+1}}\leq t<1$, there always exist a $K-$sparse $\x$ and a matrix $\A$ satisfying $\delta_{K+1}(\A)=t$ for which OMP may fail to recover the signal $\x$. Our constraints on the minimum magnitude of nonzero elements of $\x$ are also much weaker than existing ones. Moreover, we propose some necessary conditions for the exact support recovery of $\x$ on the minimum magnitude of the nonzero elements of $\x$.

Connected through hyperedge HE-1 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit"):
  with members:
    - "a sharp recovery condition for block sparse signals by block orthogonal multi matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "perturbation analysis of orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"

Connected through hyperedge HE-2 (induced by source paper "nearly optimal bounds for orthogonal least squares"):
  with members:
    - "on the exact recovery condition of simultaneous orthogonal matching pursuit"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "perturbation analysis of orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "improved sufficient conditions for sparse recovery with generalized orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "support recovery with orthogonal matching pursuit in the presence of noise"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "generalized orthogonal matching pursuit"

Connected through hyperedge HE-4 (induced by source paper "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"):
  with members:
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "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)