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: "learning quadrangulated patches for 3d shape parameterization and completion"
  Abstract: We propose a novel 3D shape parameterization by surface patches, that are oriented by 3D mesh quadrangulation of the shape. By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape. Unlike previous methods, we are able to encode surface patches of variable size as determined by the user. We propose novel methods for dictionary learning and patch reconstruction based on the query of a noisy input patch with holes. We evaluate the patch dictionary towards various applications in 3D shape inpainting, denoising and compression. Our method is able to predict missing vertices and inpaint moderately sized holes. We demonstrate a complete pipeline for reconstructing the 3D mesh from the patch encoding. We validate our shape parameterization and reconstruction methods on both synthetic shapes and real world scans. We show that our patch dictionary performs successful shape completion of complicated surface textures.

Connected through hyperedge HE-1 (induced by source paper "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (induced by source paper "learning quadrangulated patches for 3d shape processing"):
  with members:
    - "octnetfusion learning depth fusion from data"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning shape correspondence with anisotropic convolutional neural networks"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "context encoders feature learning by inpainting"
    - "pointnet deep learning on point sets for 3d classification and segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning 3d shapes as multi layered height maps using 2d convolutional networks"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mask r cnn"
    - "orientation boosted voxel nets for 3d object recognition"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "octnet learning deep 3d representations at high resolutions"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-4 (induced by source paper "pcn point completion network"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "tangent convolutions for dense prediction in 3d"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "deformable shape completion with graph convolutional autoencoders"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "deformable shape completion with graph convolutional autoencoders"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "variational autoencoders for deforming 3d mesh models"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

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)