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 completion using 3d encoder predictor cnns and shape synthesis"
  Abstract: We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution -- but complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct fine-scale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data.

Connected through hyperedge HE-1 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (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"
    - "learning quadrangulated patches for 3d shape parameterization and completion"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "scannet richly annotated 3d reconstructions of indoor scenes"):
  with members:
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "semantic scene completion from a single depth image"
    - "learning to navigate the energy landscape"
    - "a large dataset of object scans"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "a robust 3d 2d interactive tool for scene segmentation and annotation"
    - "volumetric and multi view cnns for object classification on 3d data"
    - "semanticfusion dense 3d semantic mapping with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "3d reconstruction of incomplete archaeological objects using a generative adversary network"):
  with members:
    - "squeeze and excitation networks"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic image inpainting with deep generative models"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "u net convolutional networks for biomedical image segmentation"
    - "shapenet an information rich 3d model repository"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "high resolution shape completion using deep neural networks for global structure and local geometry inference"):
  with members:
    - "vconv dae deep volumetric shape learning without object labels"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "shape completion enabled robotic grasping"
    - "shapenet an information rich 3d model repository"
    - "renet a recurrent neural network based alternative to convolutional networks"
    - "multimodal deep learning for robust rgb d object recognition"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-6 (induced by source paper "hierarchical detail enhancing mesh based shape generation with 3d generative adversarial network"):
  with members:
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "interactive 3d modeling with a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "shapenet an information rich 3d model repository"
    - "adam a method for stochastic optimization"

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)