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 discretization free sparse and parametric approach for linear array signal processing"
  Abstract: Direction of arrival (DOA) estimation in array pro- cessing using uniform/sparse linear arrays is concerned in this pa- per. While sparse methods via approximate parameter discretiza- tion have been popular in the past decade, the discretization may cause problems, e.g., modeling error and increased computations due to dense sampling. In this paper, an exact discretization- free method, named as sparse and parametric approach (SPA), is proposed for uniform and sparse linear arrays. SPA carries out parameter estimation in the continuous range based on well- established covariance fitting criteria and convex optimization. It guarantees to produce a sparse parameter estimate without discretization required by existing sparse methods. Theoretical analysis shows that the SPA parameter estimator is a large- snapshot realization of the maximum likelihood estimator and is statistically consistent (in the number of snapshots) under uncorrelated sources. Other merits of SPA include improved resolution, applicability to arbitrary number of snapshots, ro- bustness to correlation of the sources and no requirement of user-parameters. Numerical simulations are carried out to verify our analysis and demonstrate advantages of SPA compared to existing methods.

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"
    - "sparse methods for direction of arrival estimation"
    - "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"

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 "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-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"
    - "continuous compressed sensing with a single or multiple measurement vectors"

Connected through hyperedge HE-5 (induced by source paper "multi snapshot newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "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"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Connected through hyperedge HE-6 (induced by source paper "newtonized orthogonal matching pursuit for line spectrum estimation with multiple measurement vectors"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "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"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"

Connected through hyperedge HE-7 (induced by source paper "on the sample complexity of multichannel frequency estimation via convex optimization"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "a high resolution doa estimation method with a family of nonconvex penalties"):
  with members:
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "iterative concave rank approximation for recovering low rank matrices"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "performance guarantees for schatten p quasi norm minimization in recovery of low rank matrices"
    - "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)