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: "localisation of directional scale discretised wavelets on the sphere"
  Abstract: Scale-discretised wavelets yield a directional wavelet framework on the sphere where a signal can be probed not only in scale and position but also in orientation. Furthermore, a signal can be synthesised from its wavelet coefficients exactly, in theory and practice (to machine precision). Scale-discretised wavelets are closely related to spherical needlets (both were developed independently at about the same time) but relax the axisymmetric property of needlets so that directional signal content can be probed. Needlets have been shown to satisfy important quasi-exponential localisation and asymptotic uncorrelation properties. We show that these properties also hold for directional scale-discretised wavelets on the sphere and derive similar localisation and uncorrelation bounds in both the scalar and spin settings. Scale-discretised wavelets can thus be considered as directional needlets.

Connected through hyperedge HE-1 (induced by source paper "divergence free polar wavelets for the analysis and representation of fluid flows"):
  with members:
    - "polar wavelets in space"

Connected through hyperedge HE-2 (induced by source paper "sparse image reconstruction on the sphere analysis and synthesis"):
  with members:
    - "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"
    - "ridgelet transform on the sphere"

Connected through hyperedge HE-3 (induced by source paper "second generation curvelets on the sphere"):
  with members:
    - "directional spin 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 "wavelet based segmentation on the sphere"):
  with members:
    - "spherical cnns on unstructured grids"
    - "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"
    - "ridgelet transform on the sphere"

Connected through hyperedge HE-5 (induced by source paper "directional spin wavelets on the sphere"):
  with members:
    - "second generation curvelets 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"

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)