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: "second generation curvelets on the sphere"
  Abstract: Curvelets are efficient to represent highly anisotropic signal content, such as a local linear and curvilinear structure. First-generation curvelets on the sphere, however, suffered from blocking artefacts. We present a new second-generation curvelet transform, where scale-discretized curvelets are constructed directly on the sphere. Scale-discretized curvelets exhibit a parabolic scaling relation, are well localized in both spatial and harmonic domains, support the exact analysis and synthesis of both scalar and spin signals, and are free of blocking artefacts. We present fast algorithms to compute the exact curvelet transform, reducing computational complexity from   $\mathcal {O}(L^5)$   to    $\mathcal {O}(L^3\log _{2}{L})$   for signals band limited at   $L$  . The implementation of these algorithms is made publicly available. Finally, we present an illustrative application demonstrating the effectiveness of curvelets for representing directional curve-like features in natural spherical images.

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"
    - "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"
    - "ridgelet transform on the sphere"

Connected through hyperedge HE-2 (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"
    - "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"
    - "ridgelet transform on the sphere"

Connected through hyperedge HE-3 (induced by source paper "directional spin wavelets on the sphere"):
  with members:
    - "localisation of directional scale discretised wavelets on the sphere"
    - "a novel sampling theorem on the rotation group"
    - "ridgelet transform on the sphere"
    - "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-4 (induced by source paper "a novel sampling theorem on the rotation group"):
  with members:
    - "gauss legendre sampling on the rotation group"
    - "directional spin wavelets on the sphere"
    - "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"
    - "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)