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: "ridgelet transform on the sphere"
  Abstract: We first revisit the spherical Radon transform, also called the Funk-Radon transform, viewing it as an axisymmetric convolution on the sphere. Viewing the spherical Radon transform in this manner leads to a straightforward derivation of its spherical harmonic representation, from which we show the spherical Radon transform can be inverted exactly for signals exhibiting antipodal symmetry. We then construct a spherical ridgelet transform by composing the spherical Radon and scale-discretised wavelet transforms on the sphere. The resulting spherical ridgelet transform also admits exact inversion for antipodal signals. The restriction to antipodal signals is expected since the spherical Radon and ridgelet transforms themselves result in signals that exhibit antipodal symmetry. Our ridgelet transform is defined natively on the sphere, probes signal content globally along great circles, does not exhibit blocking artefacts, does not rely of any ad hoc parameters and exhibits an explicit inverse transform. No alternative ridgelet construction on the sphere satisfies all of these properties. Our implementation of the spherical Radon and ridgelet transforms is made publicly available. Finally, we illustrate the effectiveness of spherical ridgelets for diffusion magnetic resonance imaging of white matter fibers in the brain.

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 "second generation curvelets on the sphere"):
  with members:
    - "localisation of directional scale discretised wavelets on the sphere"
    - "directional spin wavelets on the sphere"
    - "a novel sampling theorem on the rotation group"
    - "on the computation of directional scale discretized wavelet transforms on the sphere"
    - "fourier laguerre transform convolution and wavelets on the ball"
    - "s2let a code to perform fast wavelet analysis on the sphere"
    - "exact wavelets on the ball"
    - "a novel sampling theorem on the sphere"

Connected through hyperedge HE-3 (induced by source paper "wavelet based segmentation on the sphere"):
  with members:
    - "spherical cnns on unstructured grids"
    - "localisation of directional scale discretised wavelets on the sphere"
    - "sparse image reconstruction on the sphere analysis and synthesis"
    - "second generation curvelets on the sphere"
    - "learning so 3 equivariant representations with spherical cnns"
    - "a three stage approach for segmenting degraded color images smoothing lifting and thresholding slat"
    - "directional spin wavelets on the sphere"
    - "a novel sampling theorem on the rotation group"

Connected through hyperedge HE-4 (induced by source paper "directional spin wavelets on the sphere"):
  with members:
    - "localisation of directional scale discretised wavelets on the sphere"
    - "second generation curvelets on the sphere"
    - "a novel sampling theorem on the rotation group"
    - "on the computation of directional scale discretized wavelet transforms on the sphere"
    - "fourier laguerre transform convolution and wavelets on the ball"
    - "s2let a code to perform fast wavelet analysis 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)