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: "support detection in super resolution"
  Abstract: We study the problem of super-resolving a superposition of point sources from noisy low-pass data with a cut-off frequency f. Solving a tractable convex program is shown to locate the elements of the support with high precision as long as they are separated by 2/f and the noise level is small with respect to the amplitude of the signal.

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"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-2 (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"
    - "compressed sensing off the grid"
    - "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-3 (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-4 (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-5 (induced by source paper "high performance quantization for spectral super resolution"):
  with members:
    - "stable super resolution limit and smallest singular value of restricted fourier matrices"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "the recoverability limit for superresolution via sparsity"
    - "noise shaping quantization methods for frame based and compressive sampling systems"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-6 (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"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "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"

Connected through hyperedge HE-7 (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"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-8 (induced by source paper "a dual certificates analysis of compressive off the grid recovery"):
  with members:
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "breaking the curse of dimensionality with convex neural networks"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "towards a mathematical theory of super resolution"
    - "analysis and extension of arc cosine kernels for large margin classification"
    - "a probabilistic and ripless theory of compressed sensing"

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)