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: "spectral generalized multi dimensional scaling"
  Abstract: Multidimensional scaling (MDS) is a family of methods that embed a given set of points into a simple, usually flat, domain. The points are assumed to be sampled from some metric space, and the mapping attempts to preserve the distances between each pair of points in the set. Distances in the target space can be computed analytically in this setting. Generalized MDS is an extension that allows mapping one metric space into another, that is, multidimensional scaling into target spaces in which distances are evaluated numerically rather than analytically. Here, we propose an efficient approach for computing such mappings between surfaces based on their natural spectral decomposition, where the surfaces are treated as sampled metric-spaces. The resulting spectral-GMDS procedure enables efficient embedding by implicitly incorporating smoothness of the mapping into the problem, thereby substantially reducing the complexity involved in its solution while practically overcoming its non-convex nature. The method is compared to existing techniques that compute dense correspondence between shapes. Numerical experiments of the proposed method demonstrate its efficiency and accuracy compared to state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "efficient and robust shape correspondence via sparsity enforced quadratic assignment"):
  with members:
    - "self supervised learning of dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "graph matching relax or not"
    - "discrete laplace beltrami operator determines discrete riemannian metric"

Connected through hyperedge HE-2 (induced by source paper "cyclic functional mapping self supervised correspondence between non isometric deformable shapes"):
  with members:
    - "lbs autoencoder self supervised fitting of articulated meshes to point clouds"
    - "3d coded 3d correspondences by deep deformation"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "geodesic convolutional neural networks on riemannian manifolds"

Connected through hyperedge HE-3 (induced by source paper "smooth shells multi scale shape registration with functional maps"):
  with members:
    - "lbs autoencoder self supervised fitting of articulated meshes to point clouds"
    - "zoomout spectral upsampling for efficient shape correspondence"
    - "divergence free shape interpolation and correspondence"
    - "continuous and orientation preserving correspondences via functional maps"
    - "3d coded 3d correspondences by deep deformation"
    - "farm functional automatic registration method for 3d human bodies"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "efficient deformable shape correspondence via kernel matching"

Connected through hyperedge HE-4 (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-5 (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"
    - "on the optimality of shape and data representation in the spectral domain"

Connected through hyperedge HE-6 (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-7 (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-8 (induced by source paper "model free consensus maximization for non rigid shapes"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "building statistical shape spaces for 3d human modeling"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "mlpnp a real time maximum likelihood solution to the perspective n point problem"
    - "template based monocular 3d shape recovery using laplacian meshes"

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.CG(Computational Geometry)