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: "factoring shape pose and layout from the 2d image of a 3d scene"
  Abstract: The goal of this paper is to take a single 2D image of a scene and recover the 3D structure in terms of a small set of factors: a layout representing the enclosing surfaces as well as a set of objects represented in terms of shape and pose. We propose a convolutional neural network-based approach to predict this representation and benchmark it on a large dataset of indoor scenes. Our experiments evaluate a number of practical design questions, demonstrate that we can infer this representation, and quantitatively and qualitatively demonstrate its merits compared to alternate representations.

Connected through hyperedge HE-1 (induced by source paper "synsin end to end view synthesis from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "differentiable surface splatting for point based geometry processing"
    - "neural point based graphics"
    - "the replica dataset a digital replica of indoor spaces"
    - "deepview view synthesis with learned gradient descent"
    - "linearized multi sampling for differentiable image transformation"
    - "neural volumes learning dynamic renderable volumes from images"
    - "semantic image synthesis with spatially adaptive normalization"

Connected through hyperedge HE-2 (induced by source paper "peeking behind objects layered depth prediction from a single image"):
  with members:
    - "layer structured 3d scene inference via view synthesis"
    - "unsupervised learning of depth and ego motion from video"
    - "shape inpainting using 3d generative adversarial network and recurrent convolutional networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semantic scene completion from a single depth image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning fine scaled depth maps from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "object driven multi layer scene decomposition from a single image"):
  with members:
    - "peeking behind objects layered depth prediction from a single image"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "deep depth completion of a single rgb d image"
    - "planercnn 3d plane detection and reconstruction from a single image"
    - "layer structured 3d scene inference via view synthesis"
    - "stereo magnification learning view synthesis using multiplane images"
    - "adversarial semantic scene completion from a single depth image"
    - "segan segmenting and generating the invisible"

Connected through hyperedge HE-4 (induced by source paper "inverse graphics unsupervised learning of 3d shapes from single images"):
  with members:
    - "bridging the elbo and mmd"
    - "preventing posterior collapse with delta vaes"
    - "structure aware shape synthesis"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "3d prnn generating shape primitives with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "facsimile fast and accurate scans from an image in less than a second"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "deep ordinal regression network for monocular depth estimation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "siclope silhouette based clothed people"
    - "detailed human avatars from monocular video"

Connected through hyperedge HE-6 (induced by source paper "poirot a rotation invariant omni directional pointnet"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "a cnn for homogneous riemannian manifolds with applications to neuroimaging"
    - "splatnet sparse lattice networks for point cloud processing"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"

Connected through hyperedge HE-7 (induced by source paper "imapper interaction guided scene mapping from monocular videos"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "detecting and recognizing human object interactions"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "mask r cnn"

Connected through hyperedge HE-8 (induced by source paper "spatial transformer for 3d point clouds"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "splatnet sparse lattice networks for point cloud processing"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "so net self organizing network for point cloud analysis"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

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)