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: "matryoshka networks predicting 3d geometry via nested shape layers"
  Abstract: In this paper, we develop novel, efficient 2D encodings for 3D geometry, which enable reconstructing full 3D shapes from a single image at high resolution. The key idea is to pose 3D shape reconstruction as a 2D prediction problem. To that end, we first develop a simple baseline network that predicts entire voxel tubes at each pixel of a reference view. By leveraging well-proven architectures for 2D pixel-prediction tasks, we attain state-of-the-art results, clearly outperforming purely voxel-based approaches. We scale this baseline to higher resolutions by proposing a memory-efficient shape encoding, which recursively decomposes a 3D shape into nested shape layers, similar to the pieces of a Matryoshka doll. This allows reconstructing highly detailed shapes with complex topology, as demonstrated in extensive experiments; we clearly outperform previous octree-based approaches despite having a much simpler architecture using standard network components. Our Matryoshka networks further enable reconstructing shapes from IDs or shape similarity, as well as shape sampling.

Connected through hyperedge HE-1 (induced by source paper "implicitly defined layers in neural networks"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "optnet differentiable optimization as a layer in neural networks"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "unsupervised learning of 3d structure from images"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "input convex neural networks"

Connected through hyperedge HE-2 (induced by source paper "probabilistic reconstruction networks for 3d shape inference from a single image"):
  with members:
    - "on the convergence of adam and beyond"
    - "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"
    - "learning shape priors for single view 3d completion and reconstruction"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"

Connected through hyperedge HE-4 (induced by source paper "pixel2mesh multi view 3d mesh generation via deformation"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "dpsnet end to end deep plane sweep stereo"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "deepmvs learning multi view stereopsis"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-5 (induced by source paper "what do single view 3d reconstruction networks learn"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "open3d a modern library for 3d data processing"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "megadepth learning single view depth prediction from internet photos"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning efficient point cloud generation for dense 3d object reconstruction"

Connected through hyperedge HE-6 (induced by source paper "cerberus a multi headed derenderer"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "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-7 (induced by source paper "self supervised 3d shape and viewpoint estimation from single images for robotics"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "metric learning for generalizing spatial relations to new objects"
    - "hierarchical surface prediction for 3d object reconstruction"

Connected through hyperedge HE-8 (induced by source paper "dr kfd a differentiable visual metric for 3d shape reconstruction"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "contextdesc local descriptor augmentation with cross modality context"
    - "what do single view 3d reconstruction networks learn"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "the perception distortion tradeoff"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

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)