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: "achieving high resolution for super resolution via reweighted atomic norm minimization"
  Abstract: The super-resolution theory developed recently by Cand\`{e}s and Fernandes-Granda aims to recover fine details of a sparse frequency spectrum from coarse scale information only. The theory was then extended to the cases with compressive samples and/or multiple measurement vectors. However, the existing atomic norm (or total variation norm) techniques succeed only if the frequencies are sufficiently separated, prohibiting commonly known high resolution. In this paper, a reweighted atomic-norm minimization (RAM) approach is proposed which iteratively carries out atomic norm minimization (ANM) with a sound reweighting strategy that enhances sparsity and resolution. It is demonstrated analytically and via numerical simulations that the proposed method achieves high resolution with application to DOA estimation.

Connected through hyperedge HE-1 (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"
    - "near minimax line spectral estimation"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "a robust sparse fourier transform in the continuous setting"):
  with members:
    - "nearly optimal sparse fourier transform"
    - "compressed sensing off the grid"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-3 (induced by source paper "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"):
  with members:
    - "super resolution on the sphere using convex optimization"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "enhancing sparsity and resolution via reweighted atomic norm minimization"
    - "super resolution radar"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "near minimax line spectral estimation"

Connected through hyperedge HE-4 (induced by source paper "enhancing sparsity and resolution via reweighted atomic norm minimization"):
  with members:
    - "distributed compressed sensing off the grid"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "direction of arrival estimation using co prime arrays a super resolution viewpoint"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"

Connected through hyperedge HE-5 (induced by source paper "on gridless sparse methods for line spectral estimation from complete and incomplete data"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "sparse inverse covariance matrix estimation using quadratic approximation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"

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)