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: "continuous compressed sensing with a single or multiple measurement vectors"
  Abstract: We consider the problem of recovering a single or multiple frequency-sparse signals, which share the same frequency components, from a subset of regularly spaced samples. The problem is referred to as continuous compressed sensing (CCS) in which the frequencies can take any values in the normalized domain [0,1). In this paper, a link between CCS and low rank matrix completion (LRMC) is established based on an $\ell_0$-pseudo-norm-like formulation, and theoretical guarantees for exact recovery are analyzed. Practically efficient algorithms are proposed based on the link and convex and nonconvex relaxations, and validated via numerical simulations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "grid less variational bayesian inference of line spectral from quantized samples"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "variational bayesian inference of line spectral estimation with multiple measurement vectors"
    - "bilinear adaptive generalized vector approximate message passing"
    - "sparse methods for direction of arrival estimation"
    - "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "hybrid approximate message passing"
    - "variational bayesian inference of line spectra"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"

Connected through hyperedge HE-3 (induced by source paper "atomic norm minimization for modal analysis from random and compressed samples"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "distributed compressed sensing off the grid"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-4 (induced by source paper "variational bayesian inference of line spectral estimation with multiple measurement vectors"):
  with members:
    - "grid less variational bayesian inference of line spectral from quantized samples"
    - "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "variational bayesian inference of line spectra"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-5 (induced by source paper "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"):
  with members:
    - "super resolution on the sphere using convex optimization"
    - "robust spectral compressed sensing via structured matrix completion"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "exact joint sparse frequency recovery via optimization methods"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "super resolution radar"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "near minimax line spectral estimation"

Connected through hyperedge HE-6 (induced by source paper "exact joint sparse frequency recovery via optimization methods"):
  with members:
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "near minimax line spectral estimation"
    - "joint sparsity recovery for spectral compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "enhancing sparsity and resolution via reweighted atomic norm minimization"):
  with members:
    - "distributed compressed sensing off the grid"
    - "robust spectral compressed sensing via structured matrix completion"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"

Connected through hyperedge HE-8 (induced by source paper "on gridless sparse methods for line spectral estimation from complete and incomplete data"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"

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)