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: "new bounds for restricted isometry constants"
  Abstract: In this paper we show that if the restricted isometry constant $\delta_k$ of the compressed sensing matrix satisfies \[ \delta_k < 0.307, \] then $k$-sparse signals are guaranteed to be recovered exactly via $\ell_1$ minimization when no noise is present and $k$-sparse signals can be estimated stably in the noisy case. It is also shown that the bound cannot be substantively improved. An explicitly example is constructed in which $\delta_{k}=\frac{k-1}{2k-1} < 0.5$, but it is impossible to recover certain $k$-sparse signals.

Connected through hyperedge HE-1 (induced by source paper "the use of mutual coherence to prove ell 1 ell 0 equivalence in classification problems"):
  with members:
    - "a probabilistic and ripless theory of compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-2 (induced by source paper "sparse methods for direction of arrival estimation"):
  with members:
    - "a compact formulation for the ell_ 2 1 mixed norm minimization problem"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"

Connected through hyperedge HE-3 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-4 (induced by source paper "a sharp bound on ric in generalized 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"
    - "improving the bound on the rip constant in generalized orthogonal matching pursuit"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "generalized orthogonal matching pursuit"

Connected through hyperedge HE-5 (induced by source paper "signal recovery under cumulative coherence"):
  with members:
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressed sensing matrices from fourier matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"

Connected through hyperedge HE-6 (induced by source paper "on the restricted isometry of the columnwise khatri rao product"):
  with members:
    - "graph sampling for covariance estimation"
    - "on the support recovery of jointly sparse gaussian sources using sparse bayesian learning"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "smoothed analysis of tensor decompositions"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "hidden cliques and the certification of the restricted isometry property"

Connected through hyperedge HE-7 (induced by source paper "recovery of signals by a weighted ell_2 ell_1 minimization under arbitrary prior support information"):
  with members:
    - "a sharp bound on ric in generalized orthogonal matching pursuit"
    - "weighted ell_1 minimization for sparse recovery under arbitrary prior information"
    - "recovery of signals under the high order rip condition via prior support information"
    - "the high order block rip condition for signal recovery"
    - "a sharp restricted isometry constant bound of orthogonal matching pursuit"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "generalized orthogonal matching pursuit"

Connected through hyperedge HE-8 (induced by source paper "weak stability of ell_1 minimization methods in sparse data reconstruction"):
  with members:
    - "the gap between the null space property and the restricted isometry property"
    - "1 bit compressive sensing reformulation and rrsp based sign recovery theory"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "on polar polytopes and the recovery of sparse representations"

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)