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: "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
  Abstract: Abstract   This work investigates the parameter estimation performance of super-resolution line spectral estimation using atomic norm minimization. The focus is on analyzing the algorithm's accuracy of inferring the frequencies and complex magnitudes from noisy observations. When the Signal-to-Noise Ratio is reasonably high and the true frequencies are separated by    O  (    1    n    )   , the atomic norm estimator is shown to localize the correct number of frequencies, each within a neighborhood of size    O  (    log  ⁡  n  /    n    3      σ  )    of one of the true frequencies. Here n is half the number of temporal samples and      σ    2      is the Gaussian noise variance. The analysis is based on a primal–dual witness construction procedure. The obtained error bound matches the Cramer–Rao lower bound up to a logarithmic factor. The relationship between resolution (separation of frequencies) and precision or accuracy of the estimator is highlighted. Our analysis also reveals that the atomic norm minimization can be viewed as a convex way to solve a      l    1     -norm regularized, nonlinear and nonconvex least-squares problem to global optimality.

Connected through hyperedge HE-1 (induced by source paper "resolution limit for line spectral estimation theory and algorithm"):
  with members:
    - "super resolution limit of the esprit algorithm"
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "the recoverability limit for superresolution via sparsity"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-2 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music"):
  with members:
    - "atomic norm denoising for complex exponentials with unknown waveform modulations"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations"):
  with members:
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "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"
    - "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"
    - "exact joint sparse frequency recovery via optimization methods"
    - "super resolution radar"

Connected through hyperedge HE-4 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution"):
  with members:
    - "a tight converse to the spectral resolution limit via convex programming"
    - "a theory of super resolution from short time fourier transform measurements"
    - "super resolution of positive sources the discrete setup"
    - "the recoverability limit for superresolution via sparsity"
    - "robust recovery of stream of pulses using convex optimization"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"

Connected through hyperedge HE-5 (induced by source paper "a tight converse to the spectral resolution limit via convex programming"):
  with members:
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-6 (induced by source paper "millimeter wave beamformed full dimensional mimo channel estimation based on atomic norm minimization"):
  with members:
    - "atomic norm denoising based joint channel estimation and faulty antenna detection for massive mimo"
    - "millimeter wave mimo channel estimation based on adaptive compressed sensing"
    - "super resolution delay doppler estimation for ofdm passive radar"
    - "non uniform linear antenna array design and optimization for millimeter wave communications"
    - "compressed sensing based multi user millimeter wave systems how many measurements are needed"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "exact joint sparse frequency recovery via optimization methods"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-7 (induced by source paper "a super resolution framework for tensor decomposition"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "super resolution on the sphere using convex optimization"
    - "robust recovery of stream of pulses using convex optimization"
    - "nuclear norm of higher order tensors"
    - "provable tensor methods for learning mixtures of generalized linear models"
    - "analyzing tensor power method dynamics in overcomplete regime"

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)