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: "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies"
  Abstract: Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in \cite{tang2012csotg} to recover $1$-dimensional spectrally sparse signal. However, in spite of existing research efforts \cite{chi2013compressive}, it was still an open problem how to formulate an equivalent positive semidefinite program for atomic norm minimization in recovering signals with $d$-dimensional ($d\geq 2$) off-the-grid frequencies. In this paper, we settle this problem by proposing equivalent semidefinite programming formulations of atomic norm minimization to recover signals with $d$-dimensional ($d\geq 2$) off-the-grid frequencies.

Connected through hyperedge HE-1 (induced by source paper "super resolution on the sphere using convex optimization"):
  with members:
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "robust recovery of stream of pulses using convex optimization"
    - "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"
    - "exact recovery of non uniform splines from the projection onto spaces of algebraic polynomials"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "near minimax line spectral estimation"
    - "sparse image reconstruction on the sphere implications of a new sampling theorem"

Connected through hyperedge HE-2 (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"
    - "achieving high resolution for super resolution via reweighted atomic norm minimization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "music for multidimensional spectral estimation stability and super resolution"):
  with members:
    - "super resolution of positive sources the discrete setup"
    - "the recoverability limit for superresolution via sparsity"
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-4 (induced by source paper "off the grid line spectrum denoising and estimation with multiple measurement vectors"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "a discretization free sparse and parametric approach for linear array signal processing"
    - "spectral compressed sensing via structured matrix completion"
    - "joint sparsity recovery for spectral compressed sensing"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"

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)