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: "compressed sensing off the grid"
  Abstract: We consider the problem of estimating the frequency components of a mixture of s complex sinusoids from a random subset of n regularly spaced samples. Unlike previous work in compressed sensing, the frequencies are not assumed to lie on a grid, but can assume any values in the normalized frequency domain [0,1]. We propose an atomic norm minimization approach to exactly recover the unobserved samples. We reformulate this atomic norm minimization as an exact semidefinite program. Even with this continuous dictionary, we show that most sampling sets of size O(s log s log n) are sufficient to guarantee the exact frequency estimation with high probability, provided the frequencies are well separated. Numerical experiments are performed to illustrate the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"):
  with members:
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "super resolution of positive sources the discrete setup"
    - "sparse spikes deconvolution on thin grids"
    - "forward backward greedy algorithms for atomic norm regularization"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "near minimax line spectral estimation"
    - "support detection in super resolution"

Connected through hyperedge HE-2 (induced by source paper "resolution limit for line spectral estimation theory and algorithm"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the geometry of off the grid compressed sensing"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "breaking the curse of dimensionality with convex neural networks"
    - "support detection in super resolution"
    - "super resolution from noisy data"
    - "recovering low rank matrices from few coefficients in any basis"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "interference removal for radar communication co existence the random scattering case"):
  with members:
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "joint design of overlaid communication systems and pulsed radars"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system"

Connected through hyperedge HE-5 (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-6 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "blind demixing and deconvolution at near optimal rate"
    - "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"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-8 (induced by source paper "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  with members:
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems"
    - "fundamental limits of training based multiuser mimo systems"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "channel estimation in massive mimo systems"
    - "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm"

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)