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: "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"
  Abstract: We develop a sampling scheme on the sphere that permits accurate computation of the spherical harmonic transform and its inverse for signals band-limited at L using only L#N# 2#N# samples. We obtain the optimal number of samples given by the degrees of freedom of the signal in harmonic space. The number of samples required in our scheme is a factor of two or four fewer than existing techniques, which require either 2L#N# 2#N# or 4L#N# 2#N# samples. We note, however, that we do not recover a sampling theorem on the sphere, where spherical harmonic transforms are theoretically exact. Nevertheless, we achieve high accuracy even for very large band-limits. For our optimal-dimensionality sampling scheme, we develop a fast and accurate algorithm to compute the spherical harmonic transform (and inverse), with computational complexity comparable with existing schemes in practice. We conduct numerical experiments to study in detail the stability, accuracy and computational complexity of the proposed transforms. We also highlight the advantages of the proposed sampling scheme and associated transforms in the context of potential applications.

Connected through hyperedge HE-1 (induced by source paper "sparse image reconstruction on the sphere analysis and synthesis"):
  with members:
    - "localisation of directional scale discretised wavelets on the sphere"
    - "second generation curvelets on the sphere"
    - "sampling sparse signals on the sphere algorithms and applications"
    - "accurate reconstruction of finite rate of innovation signals on the sphere"
    - "gauss legendre sampling on the rotation group"
    - "consistent discretization and minimization of the l1 norm on manifolds"
    - "directional spin wavelets on the sphere"
    - "a novel sampling theorem on the rotation group"

Connected through hyperedge HE-2 (induced by source paper "efficient computation of slepian functions for arbitrary regions on the sphere"):
  with members:
    - "a novel sampling theorem on the sphere"

Connected through hyperedge HE-3 (induced by source paper "iterative residual fitting for spherical harmonic transform of band limited signals on the sphere generalization and analysis"):
  with members:
    - "a novel sampling theorem on the sphere"

Connected through hyperedge HE-4 (induced by source paper "optimal dimensionality sampling on the sphere improvements and variations"):
  with members:
    - "a novel sampling theorem on the sphere"

Connected through hyperedge HE-5 (induced by source paper "sampling sparse signals on the sphere algorithms and applications"):
  with members:
    - "super resolution on the sphere using convex optimization"
    - "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-6 (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"
    - "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"
    - "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies"

Connected through hyperedge HE-7 (induced by source paper "a novel sampling theorem on the rotation group"):
  with members:
    - "second generation curvelets on the sphere"
    - "gauss legendre sampling on the rotation group"
    - "directional spin wavelets on the sphere"
    - "on the computation of directional scale discretized wavelet transforms on the sphere"
    - "exact wavelets on the ball"
    - "a novel sampling theorem on the sphere"

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)