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: "sparse methods for direction of arrival estimation"
  Abstract: Abstract   Direction-of-arrival (DOA) estimation refers to the process of retrieving the direction information of several electromagnetic waves/sources from the outputs of a number of receiving antennas that form a sensor array. DOA estimation is a major problem in array signal processing and has wide applications in radar, sonar, wireless communications, etc. With the development of sparse representation and compressed sensing, the last decade has witnessed a tremendous advance in this research topic. The purpose of this article is to provide an overview of these sparse methods for DOA estimation, with a particular highlight on the recently developed gridless sparse methods, e.g., those based on covariance fitting and the atomic norm. Several future research directions are also discussed.

Connected through hyperedge HE-1 (induced by source paper "expectation propagation line spectral estimation"):
  with members:
    - "a unified bayesian inference framework for generalized linear models"
    - "bilinear adaptive generalized vector approximate message passing"
    - "multi snapshot 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"

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"
    - "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"
    - "vector approximate message passing for the generalized linear model"

Connected through hyperedge HE-3 (induced by source paper "atomic norm minimization for modal analysis from random and compressed samples"):
  with members:
    - "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"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"

Connected through hyperedge HE-4 (induced by source paper "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Connected through hyperedge HE-5 (induced by source paper "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "on the sample complexity of multichannel frequency estimation via convex optimization"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Connected through hyperedge HE-6 (induced by source paper "wideband massive mimo channel estimation via sequential atomic norm minimization"):
  with members:
    - "parametric channel estimation for massive mimo"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "joint sparsity recovery for spectral compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-7 (induced by source paper "on the sample complexity of multichannel frequency estimation via convex optimization"):
  with members:
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "the restricted isometry property of subsampled fourier matrices"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "a discretization free sparse and parametric approach for linear array signal processing"

Connected through hyperedge HE-8 (induced by source paper "frequency selective vandermonde decomposition of toeplitz matrices with applications"):
  with members:
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "spectral super resolution with prior knowledge"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"

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)