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: "coupled quasi harmonic bases"
  Abstract: The use of Laplacian eigenbases has been shown to be fruitful in many computer graphics applications. Today, state-of-the-art approaches to shape analysis, synthesis, and correspondence rely on these natural harmonic bases that allow using classical tools from harmonic analysis on manifolds. However, many applications involving multiple shapes are obstacled by the fact that Laplacian eigenbases computed independently on different shapes are often incompatible with each other. In this paper, we propose the construction of common approximate eigenbases for multiple shapes using approximate joint diagonalization algorithms. We illustrate the benefits of the proposed approach on tasks from shape editing, pose transfer, correspondence, and similarity.

Connected through hyperedge HE-1 (induced by source paper "operatornet recovering 3d shapes from difference operators"):
  with members:
    - "multi chart generative surface modeling"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "3d coded 3d correspondences by deep deformation"
    - "unsupervised deep learning for structured shape matching"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "deformable shape completion with graph convolutional autoencoders"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-2 (induced by source paper "zoomout spectral upsampling for efficient shape correspondence"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "unsupervised deep learning for structured shape matching"
    - "fast and accurate intrinsic symmetry detection"
    - "farm functional automatic registration method for 3d human bodies"
    - "reversible harmonic maps between discrete surfaces"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "localized manifold harmonics for spectral shape analysis"
    - "point wise map recovery and refinement from functional correspondence"

Connected through hyperedge HE-3 (induced by source paper "shape analysis via functional map construction and bases pursuit"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "robust structure based shape correspondence"
    - "point wise map recovery and refinement from functional correspondence"
    - "functional correspondence by matrix completion"

Connected through hyperedge HE-4 (induced by source paper "geometry aware graph transforms for light field compact representation"):
  with members:
    - "graph fourier transform with negative edges for depth image coding"
    - "learning based view synthesis for light field cameras"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-5 (induced by source paper "unsupervised deep learning for structured shape matching"):
  with members:
    - "continuous and orientation preserving correspondences via functional maps"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "partial functional correspondence"

Connected through hyperedge HE-6 (induced by source paper "functional maps representation on product manifolds"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "composition operators matrix representation and the finite section method a theoretical framework for maps between shapes"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "geometric deep learning going beyond euclidean data"
    - "localized manifold harmonics for spectral shape analysis"
    - "hamiltonian operator for spectral shape analysis"
    - "efficient globally optimal 2d to 3d deformable shape matching"
    - "geodesic distance descriptors"

Connected through hyperedge HE-7 (induced by source paper "latent space representation for shape analysis and learning"):
  with members:
    - "learning representations and generative models for 3d point clouds"
    - "geometric deep learning going beyond euclidean data"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "adam a method for stochastic optimization"

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)