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: "coherence pattern guided compressive sensing with unresolved grids"
  Abstract: Highly coherent sensing matrices arise in discretization of continuum imaging problems such as radar and medical imaging when the grid spacing is below the Rayleigh threshold. #R##N#Algorithms based on techniques of band exclusion (BE) and local optimization (LO) are proposed to deal with such coherent sensing matrices. These techniques are embedded in the existing compressed sensing algorithms such as Orthogonal Matching Pursuit (OMP), Subspace Pursuit (SP), Iterative Hard Thresholding (IHT), Basis Pursuit (BP) and Lasso, and result in the modified algorithms BLOOMP, BLOSP, BLOIHT, BP-BLOT and Lasso-BLOT, respectively. #R##N#Under appropriate conditions, it is proved that BLOOMP can reconstruct sparse, widely separated objects up to one Rayleigh length in the Bottleneck distance {\em independent} of the grid spacing. One of the most distinguishing attributes of BLOOMP is its capability of dealing with large dynamic ranges. #R##N#The BLO-based algorithms are systematically tested with respect to four performance metrics: dynamic range, noise stability, sparsity and resolution. With respect to dynamic range and noise stability, BLOOMP is the best performer. With respect to sparsity, BLOOMP is the best performer for high dynamic range while for dynamic range near unity BP-BLOT and Lasso-BLOT with the optimized regularization parameter have the best performance. In the noiseless case, BP-BLOT has the highest resolving power up to certain dynamic range. #R##N#The algorithms BLOSP and BLOIHT are good alternatives to #R##N#BLOOMP and BP/Lasso-BLOT: they are faster than both BLOOMP and BP/Lasso-BLOT and shares, to a lesser degree, BLOOMP's amazing attribute with respect to dynamic range. #R##N#Detailed comparisons with existing algorithms such as Spectral Iterative Hard Thresholding (SIHT) and the frame-adapted BP are given.

Connected through hyperedge HE-1 (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 extremal functions and the condition number of vandermonde matrices"
    - "super resolution from noisy data"

Connected through hyperedge HE-2 (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-3 (induced by source paper "compressive parameter estimation via k median clustering"):
  with members:
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-4 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing"):
  with members:
    - "a general sequential delay doppler estimation scheme for sub nyquist pulse doppler radar"
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "gridless quadrature compressive sampling with interpolated array technique"
    - "quadrature compressive sampling for radar signals"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"

Connected through hyperedge HE-5 (induced by source paper "sensor calibration for off the grid spectral estimation"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind gain and phase calibration via sparse spectral methods"
    - "from blind deconvolution to blind super resolution through convex programming"
    - "non convex phase retrieval from stft measurements"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "leveraging diversity and sparsity in blind deconvolution"
    - "optimal sample complexity for blind gain and phase calibration"

Connected through hyperedge HE-6 (induced by source paper "stable super resolution limit and smallest singular value of restricted fourier matrices"):
  with members:
    - "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"
    - "near minimax line spectral estimation"
    - "towards a mathematical theory of super resolution"
    - "mismatch and resolution in compressive imaging"

Connected through hyperedge HE-7 (induced by source paper "fourier sparse interpolation without a frequency gap"):
  with members:
    - "a robust sparse fourier transform in the continuous setting"
    - "sparse fourier transform in any constant dimension with nearly optimal sample complexity in sublinear time"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimal approximate matrix product in terms of stable rank"
    - "super resolution off the grid"
    - "sketching as a tool for numerical linear algebra"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "near minimax line spectral estimation"

Connected through hyperedge HE-8 (induced by source paper "newtonized orthogonal matching pursuit frequency estimation over the continuum"):
  with members:
    - "compressive parameter estimation in awgn"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"
    - "orthogonal matching pursuit with replacement"

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)