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: "sparse image reconstruction on the sphere implications of a new sampling theorem"
  Abstract: We study the impact of sampling theorems on the fidelity of sparse image reconstruction on the sphere. We discuss how a reduction in the number of samples required to represent all information content of a band-limited signal acts to improve the fidelity of sparse image reconstruction, through both the dimensionality and sparsity of signals. To demonstrate this result, we consider a simple inpainting problem on the sphere and consider images sparse in the magnitude of their gradient. We develop a framework for total variation inpainting on the sphere, including fast methods to render the inpainting problem computationally feasible at high resolution. Recently a new sampling theorem on the sphere was developed, reducing the required number of samples by a factor of two for equiangular sampling schemes. Through numerical simulations, we verify the enhanced fidelity of sparse image reconstruction due to the more efficient sampling of the sphere provided by the new sampling theorem.

Connected through hyperedge HE-1 (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"
    - "fast approximation of matrix coherence and statistical leverage"
    - "a novel sampling theorem on the sphere"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (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-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 "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"
    - "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"
    - "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"
    - "precise semidefinite programming formulation of atomic norm minimization for recovering d dimensional d geq 2 off the grid frequencies"

Connected through hyperedge HE-5 (induced by source paper "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"):
  with members:
    - "exact recovery of non uniform splines from the projection onto spaces of algebraic polynomials"
    - "towards a mathematical theory of super resolution"
    - "a novel sampling theorem on the sphere"

Connected through hyperedge HE-6 (induced by source paper "an optimal dimensionality sampling scheme on the sphere with fast spherical harmonic transforms"):
  with members:
    - "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)