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: "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
  Abstract: The Vandermonde decomposition of Toeplitz matrices, discovered by Carath\'{e}odory and Fej\'{e}r in the 1910s and rediscovered by Pisarenko in the 1970s, forms the basis of modern subspace methods for 1D frequency estimation. Many related numerical tools have also been developed for multidimensional (MD), especially 2D, frequency estimation; however, a fundamental question has remained unresolved as to whether an analog of the Vandermonde decomposition holds for multilevel Toeplitz matrices in the MD case. In this paper, an affirmative answer to this question and a constructive method for finding the decomposition are provided when the matrix rank is lower than the dimension of each Toeplitz block. A numerical method for searching for a decomposition is also proposed when the matrix rank is higher. The new results are applied to studying MD frequency estimation within the recent super-resolution framework. A precise formulation of the atomic $\ell_0$ norm is derived using the Vandermonde decomposition. Practical algorithms for frequency estimation are proposed based on relaxation techniques. Extensive numerical simulations are provided to demonstrate the effectiveness of these algorithms compared to the existing atomic norm and subspace methods.

Connected through hyperedge HE-1 (induced by source paper "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm"):
  with members:
    - "multi target position and velocity estimation using ofdm communication signals"
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "near optimal sample complexity for convex tensor completion"
    - "millimeter wave beamformed full dimensional mimo channel estimation based on atomic norm minimization"
    - "super resolution delay doppler estimation for ofdm passive radar"
    - "rank determination for low rank data completion"
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor"

Connected through hyperedge HE-2 (induced by source paper "compressive multidimensional harmonic retrieval with prior knowledge"):
  with members:
    - "multi target position and velocity estimation using ofdm communication signals"
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "super resolution delay doppler estimation for ofdm passive radar"
    - "frequency selective vandermonde decomposition of toeplitz matrices with applications"
    - "spectral super resolution with prior knowledge"
    - "exact joint sparse frequency recovery via optimization methods"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-3 (induced by source paper "multidimensional variational line spectra estimation"):
  with members:
    - "grid less variational bayesian inference of line spectral from quantized samples"
    - "efficient downlink channel reconstruction for fdd multi antenna systems"
    - "variational bayesian inference of line spectra"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-4 (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"
    - "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"
    - "continuous compressed sensing with a single or multiple measurement vectors"

Connected through hyperedge HE-5 (induced by source paper "blind two dimensional super resolution and its performance guarantee"):
  with members:
    - "super resolution mimo radar"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "self calibration and biconvex compressive sensing"
    - "super resolution on the sphere using convex optimization"
    - "the recoverability limit for superresolution via sparsity"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"

Connected through hyperedge HE-6 (induced by source paper "super resolution mmwave channel estimation using atomic norm minimization"):
  with members:
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "hybrid mimo architectures for millimeter wave communications phase shifters or switches"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "millimeter wave channel modeling and cellular capacity evaluation"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "compressed sensing off the grid"

Connected through hyperedge HE-7 (induced by source paper "wideband massive mimo channel estimation via sequential atomic norm minimization"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "parametric channel estimation for massive mimo"
    - "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"
    - "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-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"
    - "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"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "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)