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: "learning shape priors for single view 3d completion and reconstruction"
  Abstract: The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. #R##N#In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.

Connected through hyperedge HE-1 (induced by source paper "meta3d single view 3d object reconstruction from shape priors in memory"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "adaptive semantic visual tree for hierarchical embeddings"
    - "meta learning with latent embedding optimization"
    - "meta learning with differentiable closed form solvers"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "domain adaptive single view 3d reconstruction"
    - "probabilistic model agnostic meta learning"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-2 (induced by source paper "shallow2deep indoor scene modeling by single image understanding"):
  with members:
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "seethrough finding chairs in heavily occluded indoor scene images"
    - "a simple neural network module for relational reasoning"
    - "roomnet end to end room layout estimation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-3 (induced by source paper "conditional single view shape generation for multi view stereo reconstruction"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "recurrent transformer networks for semantic correspondence"
    - "so net self organizing network for point cloud analysis"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning to reconstruct shapes from unseen classes"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-4 (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-5 (induced by source paper "self supervised learning of 3d objects from natural images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "learning to infer implicit surfaces without 3d supervision"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "learning view priors for single view 3d reconstruction"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-6 (induced by source paper "front2back single view 3d shape reconstruction via front to back prediction"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "moulding humans non parametric 3d human shape estimation from single images"
    - "3dn 3d deformation network"
    - "multimodal unsupervised image to image translation"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "learning implicit fields for generative shape modeling"
    - "siclope silhouette based clothed people"

Connected through hyperedge HE-7 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "triplet center loss for multi view 3d object retrieval"
    - "3d pose estimation for fine grained object categories"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "ipose instance aware 6d pose estimation of partly occluded objects"

Connected through hyperedge HE-8 (induced by source paper "deep stereo using adaptive thin volume representation with uncertainty awareness"):
  with members:
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "dpsnet end to end deep plane sweep stereo"
    - "point based multi view stereo network"
    - "recurrent mvsnet for high resolution multi view stereo depth inference"
    - "deeptam deep tracking and mapping"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"

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)