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 to generate and reconstruct 3d meshes with only 2d supervision"
  Abstract: We present a unified framework tackling two problems: class-specific 3D reconstruction from a single image, and generation of new 3D shape samples. These tasks have received considerable attention recently; however, existing approaches rely on 3D supervision, annotation of 2D images with keypoints or poses, and/or training with multiple views of each object instance. Our framework is very general: it can be trained in similar settings to these existing approaches, while also supporting weaker supervision scenarios. Importantly, it can be trained purely from 2D images, without ground-truth pose annotations, and with a single view per instance. We employ meshes as an output representation, instead of voxels used in most prior work. This allows us to exploit shading information during training, which previous 2D-supervised methods cannot. Thus, our method can learn to generate and reconstruct concave object classes. We evaluate our approach on synthetic data in various settings, showing that (i) it learns to disentangle shape from pose; (ii) using shading in the loss improves performance; (iii) our model is comparable or superior to state-of-the-art voxel-based approaches on quantitative metrics, while producing results that are visually more pleasing; (iv) it still performs well when given supervision weaker than in prior works.

Connected through hyperedge HE-1 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency"):
  with members:
    - "photo geometric autoencoding to learn 3d objects from unlabelled images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "scops self supervised co part segmentation"
    - "learning to infer implicit surfaces without 3d supervision"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning view priors for single view 3d reconstruction"

Connected through hyperedge HE-2 (induced by source paper "hologan unsupervised learning of 3d representations from natural images"):
  with members:
    - "representation learning by rotating your faces"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "spectral normalization for generative adversarial networks"
    - "towards open set identity preserving face synthesis"

Connected through hyperedge HE-3 (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"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "multi view silhouette and depth decomposition for high resolution 3d object representation"

Connected through hyperedge HE-4 (induced by source paper "transformable bottleneck networks"):
  with members:
    - "3dn 3d deformation network"
    - "visual object networks image generation with disentangled 3d representation"
    - "geometry aware deep network for single image novel view synthesis"
    - "layer structured 3d scene inference via view synthesis"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "megadepth learning single view depth prediction from internet photos"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-5 (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"
    - "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"
    - "learning shape priors for single view 3d completion and reconstruction"

Connected through hyperedge HE-6 (induced by source paper "learning single image 3d reconstruction by generative modelling of shape pose and shading"):
  with members:
    - "structure aware shape synthesis"
    - "visual object networks image generation with disentangled 3d representation"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "multiresolution tree networks for 3d point cloud processing"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-7 (induced by source paper "data efficient learning for sim to real robotic grasping using deep point cloud prediction networks"):
  with members:
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "grasp2vec learning object representations from self supervised grasping"
    - "3d shape perception from monocular vision touch and shape priors"
    - "multiresolution tree networks for 3d point cloud processing"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "parsing geometry using structure aware shape templates"

Connected through hyperedge HE-8 (induced by source paper "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "visual object networks image generation with disentangled 3d representation"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"

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)