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: "super resolution extremal functions and the condition number of vandermonde matrices"
  Abstract: Super-resolution is a fundamental task in imaging, where the goal is to extract fine-grained structure from coarse-grained measurements. Here we are interested in a popular mathematical abstraction of this problem that has been widely studied in the statistics, signal processing and machine learning communities. We exactly resolve the threshold at which noisy super-resolution is possible. In particular, we establish a sharp phase transition for the relationship between the cutoff frequency ($m$) and the separation ($\Delta$). If $m > 1/\Delta + 1$, our estimator converges to the true values at an inverse polynomial rate in terms of the magnitude of the noise. And when $m < (1-\epsilon) /\Delta$ no estimator can distinguish between a particular pair of $\Delta$-separated signals even if the magnitude of the noise is exponentially small. #R##N#Our results involve making novel connections between {\em extremal functions} and the spectral properties of Vandermonde matrices. We establish a sharp phase transition for their condition number which in turn allows us to give the first noise tolerance bounds for the matrix pencil method. Moreover we show that our methods can be interpreted as giving preconditioners for Vandermonde matrices, and we use this observation to design faster algorithms for super-resolution. We believe that these ideas may have other applications in designing faster algorithms for other basic tasks in signal processing.

Connected through hyperedge HE-1 (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"
    - "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 "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-3 (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"
    - "near minimax line spectral estimation"

Connected through hyperedge HE-4 (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"
    - "compressed sensing off the grid"

Connected through hyperedge HE-5 (induced by source paper "conditioning of restricted fourier matrices and super resolution of music"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "elementary l error estimates for super resolution de noising"
    - "music for multidimensional spectral estimation stability and super resolution"
    - "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 from noisy data"
    - "coherence pattern guided compressive sensing with unresolved grids"

Connected through hyperedge HE-6 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  with members:
    - "relating leverage scores and density using regularized christoffel functions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "active regression via linear sample sparsification"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "principal component projection without principal component analysis"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-7 (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"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "the recoverability limit for superresolution via sparsity"

Connected through hyperedge HE-8 (induced by source paper "multi kernel unmixing and super resolution using the modified matrix pencil method"):
  with members:
    - "stable separation and super resolution of mixture models"
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "near minimax line spectral estimation"
    - "support detection in super resolution"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

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)