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: "super resolution on the sphere using convex optimization"
  Abstract: This paper considers the problem of recovering an ensemble of Diracs on a sphere from its low resolution measurements. The Diracs can be located at any location on the sphere, not necessarily on a grid. We show that under a separation condition, one can recover the ensemble with high precision by a three-stage algorithm, which consists of solving a semi-definite program, root finding and least-square fitting. The algorithm's computation time depends solely on the number of measurements, and not on the required solution accuracy. We also show that in the special case of non-negative ensembles, a sparsity condition is sufficient for recovery. Furthermore, in the discrete setting, we estimate the recovery error in the presence of noise as a function of the noise level and the super-resolution factor.

Connected through hyperedge HE-1 (induced by source paper "blind two dimensional super resolution and its performance guarantee"):
  with members:
    - "super resolution mimo radar"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "self calibration and biconvex compressive sensing"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "the recoverability limit for superresolution via sparsity"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"

Connected through hyperedge HE-2 (induced by source paper "fourier phase retrieval uniqueness and algorithms"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "bispectrum inversion with application to multireference alignment"
    - "on fienup methods for sparse phase retrieval"
    - "on signal reconstruction from frog measurements"
    - "on the 2d phase retrieval problem"
    - "multiple illumination phaseless super resolution mips with applications to phaseless doa estimation and diffraction imaging"
    - "on the uniqueness of frog methods"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"

Connected through hyperedge HE-3 (induced by source paper "a super resolution framework for tensor decomposition"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "learning one hidden layer neural networks with landscape design"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "robust recovery of stream of pulses using convex optimization"
    - "nuclear norm of higher order tensors"
    - "provable tensor methods for learning mixtures of generalized linear models"
    - "analyzing tensor power method dynamics in overcomplete regime"

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

Connected through hyperedge HE-5 (induced by source paper "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"):
  with members:
    - "self calibration and biconvex compressive sensing"
    - "stable separation and super resolution of mixture models"
    - "a unified framework for identifiability analysis in bilinear inverse problems with applications to subspace and sparsity models"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "joint sparsity recovery for spectral compressed sensing"
    - "support detection in super resolution"

Connected through hyperedge HE-6 (induced by source paper "adapting to unknown noise level in sparse deconvolution"):
  with members:
    - "support recovery for sparse deconvolution of positive measures"
    - "sparse spikes deconvolution on thin grids"
    - "robust recovery of stream of pulses using convex optimization"
    - "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"
    - "near minimax line spectral estimation"
    - "support detection in super resolution"
    - "compressed sensing off the grid"

Connected through hyperedge HE-7 (induced by source paper "sampling sparse signals on the sphere algorithms and applications"):
  with members:
    - "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"
    - "towards a mathematical theory of super resolution"
    - "a novel sampling theorem on the sphere"

Connected through hyperedge HE-8 (induced by source paper "accurate reconstruction of finite rate of innovation signals on the sphere"):
  with members:
    - "sampling sparse signals on the sphere algorithms and applications"
    - "towards a mathematical theory of super resolution"

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)