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: "a novel sampling theorem on the sphere"
  Abstract: We develop a novel sampling theorem on the sphere and corresponding fast algorithms by associating the sphere with the torus through a periodic extension. The fundamental property of any sampling theorem is the number of samples required to represent a band-limited signal. To represent exactly a signal on the sphere band-limited at L, all sampling theorems on the sphere require O(L2) samples. However, our sampling theorem requires less than half the number of samples of other equiangular sampling theorems on the sphere and an asymptotically identical, but smaller, number of samples than the Gauss-Legendre sampling theorem. The complexity of our algorithms scale as O(L3), however, the continual use of fast Fourier transforms reduces the constant prefactor associated with the asymptotic scaling considerably, resulting in algorithms that are fast. Furthermore, we do not require any precomputation and our algorithms apply to both scalar and spin functions on the sphere without any change in computational complexity or computation time. We make our implementation of these algorithms available publicly and perform numerical experiments demonstrating their speed and accuracy up to very high band-limits. Finally, we highlight the advantages of our sampling theorem in the context of potential applications, notably in the field of compressive sampling.

Connected through hyperedge HE-1 (induced by source paper "variable viewpoint representations for 3d object recognition"):
  with members:
    - "spherical cnns on unstructured grids"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "learning so 3 equivariant representations with spherical cnns"
    - "the expressive power of neural networks a view from the width"
    - "sampling sparse signals on the sphere algorithms and applications"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-2 (induced by source paper "sensing matrix design and sparse recovery on the sphere and the rotation group"):
  with members:
    - "coherence optimization and best complex antipodal spherical codes"
    - "compressed sensing matrices from fourier matrices"
    - "sparse image reconstruction on the sphere implications of a new sampling theorem"
    - "fast approximation of matrix coherence and statistical leverage"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "localisation of directional scale discretised wavelets on the sphere"):
  with members:
    - "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"

Connected through hyperedge HE-4 (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-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "efficient computation of slepian functions for arbitrary regions on the sphere"):
  with members:
    - "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"

Connected through hyperedge HE-7 (induced by source paper "iterative residual fitting for spherical harmonic transform of band limited signals on the sphere generalization and analysis"):
  with members:
    - "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"

Connected through hyperedge HE-8 (induced by source paper "optimal dimensionality sampling on the sphere improvements and variations"):
  with members:
    - "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"

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)