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: "on the optimality of shape and data representation in the spectral domain"
  Abstract: A proof of the optimality of the eigenfunctions of the Laplace-Beltrami operator (LBO) in representing smooth functions on surfaces is provided and adapted to the field of applied shape and data analysis. It is based on the Courant-Fischer min-max principle adapted to our case. % The theorem we present supports the new trend in geometry processing of treating geometric structures by using their projection onto the leading eigenfunctions of the decomposition of the LBO. Utilisation of this result can be used for constructing numerically efficient algorithms to process shapes in their spectrum. We review a couple of applications as possible practical usage cases of the proposed optimality criteria. % We refer to a scale invariant metric, which is also invariant to bending of the manifold. This novel pseudo-metric allows constructing an LBO by which a scale invariant eigenspace on the surface is defined. We demonstrate the efficiency of an intermediate metric, defined as an interpolation between the scale invariant and the regular one, in representing geometric structures while capturing both coarse and fine details. Next, we review a numerical acceleration technique for classical scaling, a member of a family of flattening methods known as multidimensional scaling (MDS). There, the optimality is exploited to efficiently approximate all geodesic distances between pairs of points on a given surface, and thereby match and compare between almost isometric surfaces. Finally, we revisit the classical principal component analysis (PCA) definition by coupling its variational form with a Dirichlet energy on the data manifold. By pairing the PCA with the LBO we can handle cases that go beyond the scope defined by the observation set that is handled by regular PCA.

Connected through hyperedge HE-1 (induced by source paper "bilateral operators for functional maps"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-2 (induced by source paper "self functional maps"):
  with members:
    - "steklov spectral geometry for extrinsic shape analysis"
    - "geodesic distance descriptors"
    - "spectral generalized multi dimensional scaling"
    - "smi 2011 full paper diffusion geometric maximally stable component detection in deformable shapes"
    - "discrete schemes for gaussian curvature and their convergence"

Connected through hyperedge HE-3 (induced by source paper "the steerable graph laplacian and its application to filtering image data sets"):
  with members:
    - "steerable principal components for space frequency localized images"
    - "fourier bessel rotational invariant eigenimages"

Connected through hyperedge HE-4 (induced by source paper "isospectralization or how to hear shape style and correspondence"):
  with members:
    - "adam a method for stochastic optimization"
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-5 (induced by source paper "self supervised learning of dense shape correspondence"):
  with members:
    - "spectral generalized multi dimensional scaling"

Connected through hyperedge HE-6 (induced by source paper "deep functional maps structured prediction for dense shape correspondence"):
  with members:
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "dense human body correspondences using convolutional networks"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-7 (induced by source paper "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"):
  with members:
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "spectrometer amortized sublinear spectral approximation of distance on graphs"
    - "point wise map recovery and refinement from functional correspondence"
    - "functional correspondence by matrix completion"
    - "matching lbo eigenspace of non rigid shapes via high order statistics"
    - "spectral generalized multi dimensional scaling"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "localized manifold harmonics for spectral shape analysis"):
  with members:
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "hamiltonian operator for spectral shape analysis"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "functional correspondence by matrix completion"
    - "coupled quasi harmonic bases"
    - "sparse modeling of intrinsic correspondences"
    - "point set registration coherent point drift"

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.CV(Computer Vision and Pattern Recognition)