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: "on gridless sparse methods for line spectral estimation from complete and incomplete data"
  Abstract: This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid-based approaches, e.g., $\ell_1$ optimization and SPICE, with an infinitely dense grid. We generalize AST (atomic-norm soft thresholding) to the case of nonconsecutively sampled data (incomplete data) inspired by recent atomic norm based techniques. We present a gridless version of SPICE (gridless SPICE, or GLS), which is applicable to both complete and incomplete data without the knowledge of noise level. We further prove the equivalence between GLS and atomic norm-based techniques under different assumptions of noise. Moreover, we extend GLS to a systematic framework consisting of model order selection and robust frequency estimation, and present feasible algorithms for AST and GLS. Numerical simulations are provided to validate our theoretical analysis and demonstrate performance of our methods compared to existing ones.

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 "functional nonlinear sparse models"):
  with members:
    - "learning power spectrum maps from quantized power measurements"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "block iterative reweighted algorithms for super resolution of spectrally sparse signals"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "computational complexity of certifying restricted isometry property"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-5 (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-6 (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"
    - "a discretization free sparse and parametric approach for linear array signal processing"

Connected through hyperedge HE-7 (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"
    - "a discretization free sparse and parametric approach for linear array signal processing"

Connected through hyperedge HE-8 (induced by source paper "joint doppler and channel estimation with nested arrays for millimeter wave communications"):
  with members:
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "what will 5g be"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "atomic norm denoising with applications to line spectral estimation"

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)