human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"):
  Abstract: The Vandermonde decomposition of Toeplitz matrices, discovered by Carath\'{e}odory and Fej\'{e}r in the 1910s and rediscovered by Pisarenko in the 1970s, forms the basis of modern subspace methods for 1D frequency estimation. Many related numerical tools have also been developed for multidimensional (MD), especially 2D, frequency estimation; however, a fundamental question has remained unresolved as to whether an analog of the Vandermonde decomposition holds for multilevel Toeplitz matrices in the MD case. In this paper, an affirmative answer to this question and a constructive method for finding the decomposition are provided when the matrix rank is lower than the dimension of each Toeplitz block. A numerical method for searching for a decomposition is also proposed when the matrix rank is higher. The new results are applied to studying MD frequency estimation within the recent super-resolution framework. A precise formulation of the atomic $\ell_0$ norm is derived using the Vandermonde decomposition. Practical algorithms for frequency estimation are proposed based on relaxation techniques. Extensive numerical simulations are provided to demonstrate the effectiveness of these algorithms compared to the existing atomic norm and subspace methods.

Includes members:
  Member "super resolution on the sphere using convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind two dimensional super resolution and its performance guarantee")
  Member "robust spectral compressed sensing via structured matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recovery analysis of damped spectrally sparse signals and its relation to music")
  Member "achieving high resolution for super resolution via reweighted atomic norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fourier sparse interpolation without a frequency gap")
  Member "exact joint sparse frequency recovery via optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate support recovery of atomic line spectral estimation a tale of resolution and precision")
  Member "continuous compressed sensing with a single or multiple measurement vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse methods for direction of arrival estimation")
  Member "enhancing sparsity and resolution via reweighted atomic norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interference removal for radar communication co existence the random scattering case")
  Member "super resolution radar":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tree search network for sparse regression")
  Member "on gridless sparse methods for line spectral estimation from complete and incomplete data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "expectation propagation line spectral estimation")

Question: Please tell me which class the source paper 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)