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: "exact solutions to super resolution on semi algebraic domains in higher dimensions"
  Abstract: We investigate the multi-dimensional Super Resolution problem on closed semi-algebraic domains for various sampling schemes such as Fourier or moments. We present a new semidefinite programming (SDP) formulation of the 1 -minimization in the space of Radon measures in the multi-dimensional frame on semi-algebraic sets. While standard approaches have focused on SDP relaxations of the dual program (a popular approach is based on Gram matrix representations), this paper introduces an exact formulation of the primal 1 -minimization exact recovery problem of Super Resolution that unleashes standard techniques (such as moment-sum-of-squares hier-archies) to overcome intrinsic limitations of previous works in the literature. Notably, we show that one can exactly solve the Super Resolution problem in dimension greater than 2 and for a large family of domains described by semi-algebraic sets.

Connected through hyperedge HE-1 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "sketching for large scale learning of mixture models"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "compressive k means"
    - "adapting to unknown noise level in sparse deconvolution"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"

Connected through hyperedge HE-2 (induced by source paper "frequency selective vandermonde decomposition of toeplitz matrices with applications"):
  with members:
    - "sparse methods for direction of arrival estimation"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "spectral super resolution with prior knowledge"
    - "on gridless sparse methods for line spectral estimation from complete and incomplete data"
    - "atomic norm denoising with applications to line spectral estimation"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"

Connected through hyperedge HE-3 (induced by source paper "compressive k means"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "greedy column subset selection new bounds and distributed algorithms"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a unified framework for approximating and clustering data"
    - "random projections for k means clustering"

Connected through hyperedge HE-4 (induced by source paper "robust recovery of stream of pulses using convex optimization"):
  with members:
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "robust recovery of positive stream of pulses"
    - "spectral super resolution with prior knowledge"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"

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)