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: "shape generation using spatially partitioned point clouds"
  Abstract: We propose a method to generate 3D shapes using point clouds. Given a point-cloud representation of a 3D shape, our method builds a kd-tree to spatially partition the points. This orders them consistently across all shapes, resulting in reasonably good correspondences across all shapes. We then use PCA analysis to derive a linear shape basis across the spatially partitioned points, and optimize the point ordering by iteratively minimizing the PCA reconstruction error. Even with the spatial sorting, the point clouds are inherently noisy and the resulting distribution over the shape coefficients can be highly multi-modal. We propose to use the expressive power of neural networks to learn a distribution over the shape coefficients in a generative-adversarial framework. Compared to 3D shape generative models trained on voxel-representations, our point-based method is considerably more light-weight and scalable, with little loss of quality. It also outperforms simpler linear factor models such as Probabilistic PCA, both qualitatively and quantitatively, on a number of categories from the ShapeNet dataset. Furthermore, our method can easily incorporate other point attributes such as normal and color information, an additional advantage over voxel-based representations.

Connected through hyperedge HE-1 (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-2 (induced by source paper "efficient learning on point clouds with basis point sets"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d coded 3d correspondences by deep deformation"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "deep sets"
    - "farm functional automatic registration method for 3d human bodies"
    - "tangent convolutions for dense prediction in 3d"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-3 (induced by source paper "multiresolution tree networks for 3d point cloud processing"):
  with members:
    - "splatnet sparse lattice networks for point cloud processing"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "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-4 (induced by source paper "p2p net bidirectional point displacement net for shape transform"):
  with members:
    - "pcpnet learning local shape properties from raw point clouds"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "unsupervised image to image translation networks"
    - "complementme weakly supervised component suggestions for 3d modeling"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"

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)