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: "towards a mathematical theory of super resolution"
  Abstract: This paper develops a mathematical theory of super-resolution. Broadly speaking, super-resolution is the problem of recovering the fine details of an object---the high end of its spectrum---from coarse scale information only---from samples at the low end of the spectrum. Suppose we have many point sources at unknown locations in $[0,1]$ and with unknown complex-valued amplitudes. We only observe Fourier samples of this object up until a frequency cut-off $f_c$. We show that one can super-resolve these point sources with infinite precision---i.e. recover the exact locations and amplitudes---by solving a simple convex optimization problem, which can essentially be reformulated as a semidefinite program. This holds provided that the distance between sources is at least $2/f_c$. This result extends to higher dimensions and other models. In one dimension for instance, it is possible to recover a piecewise smooth function by resolving the discontinuity points with infinite precision as well. We also show that the theory and methods are robust to noise. In particular, in the discrete setting we develop some theoretical results explaining how the accuracy of the super-resolved signal is expected to degrade when both the noise level and the {\em super-resolution factor} vary.

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 "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-4 (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-5 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain"):
  with members:
    - "a sampling framework for solving physics driven inverse source problems"
    - "on unlimited sampling"
    - "shift invariant and sampling spaces associated with the special affine fourier transform"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "frida fri based doa estimation for arbitrary array layouts"
    - "a theory of super resolution from short time fourier transform measurements"
    - "identification of linear time varying systems through waveform diversity"
    - "super resolution in phase space"

Connected through hyperedge HE-6 (induced by source paper "on the stable resolution limit of total variation regularization for spike deconvolution"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "data driven estimation of sinusoid frequencies"):
  with members:
    - "a learning based framework for line spectra super resolution"
    - "direction of arrival estimation for multiple sound sources using convolutional recurrent neural network"
    - "broadband doa estimation using convolutional neural networks trained with noise signals"
    - "maximal sparsity with deep networks"
    - "adapting to unknown noise level in sparse deconvolution"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "super resolution extremal functions and the condition number of vandermonde matrices"

Connected through hyperedge HE-8 (induced by source paper "super resolution limit of the esprit algorithm"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "elementary l error estimates for super resolution de noising"
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "deterministic performance analysis of subspace methods for cisoid parameter estimation"
    - "super resolution of positive sources the discrete setup"
    - "the recoverability limit for superresolution via sparsity"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"

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)