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: "vconv dae deep volumetric shape learning without object labels"
  Abstract: With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation.

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 "multimodal shape completion via conditional generative adversarial networks"):
  with members:
    - "morphing and sampling network for dense point cloud completion"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "dense 3d object reconstruction from a single depth view"
    - "unpaired point cloud completion on real scans using adversarial training"
    - "visual object networks image generation with disentangled 3d representation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "p2p net bidirectional point displacement net for shape transform"

Connected through hyperedge HE-3 (induced by source paper "energy based processes for exchangeable data"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "neural similarity learning"
    - "the functional neural process"
    - "conditional neural processes"
    - "pointgrow autoregressively learned point cloud generation with self attention"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-4 (induced by source paper "unsupervised multi task feature learning on point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "3d point capsule networks"
    - "clustering with deep learning taxonomy and new methods"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep clustering for unsupervised learning of visual features"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-5 (induced by source paper "dense 3d object reconstruction from a single depth view"):
  with members:
    - "defo net learning body deformation using generative adversarial networks"
    - "mcgan mean and covariance feature matching gan"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "progressive growing of gans for improved quality stability and variation"
    - "end to end 3d face reconstruction with deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "3d point capsule networks"
    - "weighted multi projection 3d point cloud denoising with estimated tangent planes"
    - "a graph cnn for 3d point cloud classification"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-7 (induced by source paper "rank3dgan semantic mesh generation using relative attributes"):
  with members:
    - "multi chart generative surface modeling"
    - "learning category specific mesh reconstruction from image collections"
    - "ranking cgans subjective control over semantic image attributes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-8 (induced by source paper "pointflow 3d point cloud generation with continuous normalizing flows"):
  with members:
    - "videoflow a flow based generative model for video"
    - "pointwise an unsupervised point wise feature learning network"
    - "waveglow a flow based generative network for speech synthesis"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep sets"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "point cloud gan"

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)