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: "improved adversarial systems for 3d object generation and reconstruction"
  Abstract: This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orientations. Our method extends previous work by employing the Wasserstein distance normalized with gradient penalization as a training objective. This enables improved generation from the joint object shape distribution. Our system can also reconstruct 3D shape from 2D images and perform shape completion from occluded 2.5D range scans. We achieve notable quantitative improvements in comparison to existing baselines

Connected through hyperedge HE-1 (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-2 (induced by source paper "geometrics exploiting geometric structure for graph encoded objects"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "3d coded 3d correspondences by deep deformation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-3 (induced by source paper "probabilistic reconstruction networks for 3d shape inference from a single image"):
  with members:
    - "on the convergence of adam and beyond"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-4 (induced by source paper "3d scene reconstruction with multi layer depth and epipolar transformers"):
  with members:
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "geometric pose affordance 3d human pose with scene constraints"
    - "peeking behind objects layered depth prediction from a single image"
    - "multiresolution tree networks for 3d point cloud processing"
    - "splatnet sparse lattice networks for point cloud processing"
    - "layer structured 3d scene inference via view synthesis"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"

Connected through hyperedge HE-5 (induced by source paper "learning to predict 3d objects with an interpolation based differentiable renderer"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "what do single view 3d reconstruction networks learn"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning category specific mesh reconstruction from image collections"
    - "3d aware scene manipulation via inverse graphics"

Connected through hyperedge HE-6 (induced by source paper "pu gan a point cloud upsampling adversarial network"):
  with members:
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "ec net an edge aware point set consolidation network"
    - "patch based progressive 3d point set upsampling"
    - "high fidelity semantic shape completion for point clouds using latent optimization"
    - "pu net point cloud upsampling network"

Connected through hyperedge HE-7 (induced by source paper "high resolution millimeter wave imaging for self driving cars"):
  with members:
    - "an adversarial super resolution remedy for radar design trade offs"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "point cloud gan"
    - "a style based generator architecture for generative adversarial networks"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "learning to see in the dark"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "kaolin a pytorch library for accelerating 3d deep learning research"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "meshcnn a network with an edge"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "pixel2mesh generating 3d mesh models from single rgb images"

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)