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: "a sharp condition for exact support recovery of sparse signals with orthogonal matching pursuit"
  Abstract: Support recovery of sparse signals from noisy measurements with orthogonal matching pursuit (OMP) has been extensively studied in the literature. In this paper, we show that for any $K$-sparse signal $\x$, if the sensing matrix $\A$ satisfies the restricted isometry property (RIP) of order $K + 1$ with restricted isometry constant (RIC) $\delta_{K+1} < 1/\sqrt {K+1}$, then under some constraint on the minimum magnitude of the nonzero elements of $\x$, the OMP algorithm exactly recovers the support of $\x$ from the measurements $\y=\A\x+\v$ in $K$ iterations, where $\v$ is the noise vector. This condition is sharp in terms of $\delta_{K+1}$ since for any given positive integer $K\geq 2$ and any $1/\sqrt{K+1}\leq t<1$, there always exist a $K$-sparse $\x$ and a matrix $\A$ satisfying $\delta_{K+1}=t$ for which OMP may fail to recover the signal $\x$ in $K$ iterations. Moreover, the constraint on the minimum magnitude of the nonzero elements of $\x$ is weaker than existing results.

Connected through hyperedge HE-1 (induced by source paper "a sharp recovery condition for sparse signals with partial support information via orthogonal matching pursuit"):
  with members:
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "recovery of signals under the high order rip condition via prior support information"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "exact recovery conditions for sparse representations with partial support information"
    - "online recovery guarantees and analytical results for omp"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "on the theoretical analysis of orthogonal matching pursuit with termination based on the residue"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-2 (induced by source paper "block compressed sensing based distributed device detection for m2m communications"):
  with members:
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "newton like method with diagonal correction for distributed optimization"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "condition numbers of gaussian random matrices"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "a novel sufficient condition for 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"
    - "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 "sharp conditions for exact support recovery of sparse signals with noise via omp"):
  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"
    - "exact recovery conditions for sparse representations with partial support information"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "new bounds for restricted isometry constants"
    - "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)