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: "mesh based autoencoders for localized deformation component analysis"
  Abstract: Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise. It also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "deep learning for 3d point clouds a survey"
    - "bsp net generating compact meshes via binary space partitioning"
    - "pixel2mesh multi view 3d mesh generation via deformation"

Connected through hyperedge HE-2 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "3dn 3d deformation network"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "irregular convolutional auto encoder on point clouds"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "cgans with projection discriminator"
    - "hierarchical graph representation learning with differentiable pooling"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "local spectral graph convolution for point set feature learning"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-4 (induced by source paper "mesh variational autoencoders with edge contraction pooling"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "deformable shape completion with graph convolutional autoencoders"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"

Connected through hyperedge HE-5 (induced by source paper "learning embedding of 3d models with quadric loss"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "scan2mesh from unstructured range scans to 3d meshes"
    - "dynamic graph cnn for learning on point clouds"
    - "structure aware generative network for 3d shape modeling"
    - "abc a big cad model dataset for geometric deep learning"
    - "deformable shape completion with graph convolutional autoencoders"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-6 (induced by source paper "3d magic mirror automatic video to 3d caricature translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "multimodal unsupervised image to image translation"
    - "alive caricature from 2d to 3d"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

Connected through hyperedge HE-7 (induced by source paper "normalnet learning based guided normal filtering for mesh denoising"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "point wise convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "disentangled representation learning for 3d face shape"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "alive caricature from 2d to 3d"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "nonlinear 3d face morphable model"
    - "sparse data driven mesh deformation"
    - "3d face morphable models in the wild"
    - "geometric deep learning going beyond euclidean data"

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.GR(Graphics)