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: "3d lmnet latent embedding matching for accurate and diverse 3d point cloud reconstruction from a single image"
  Abstract: 3D reconstruction from single view images is an ill-posed problem. Inferring the hidden regions from self-occluded images is both challenging and ambiguous. We propose a two-pronged approach to address these issues. To better incorporate the data prior and generate meaningful reconstructions, we propose 3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first train a 3D point cloud auto-encoder and then learn a mapping from the 2D image to the corresponding learnt embedding. To tackle the issue of uncertainty in the reconstruction, we predict multiple reconstructions that are consistent with the input view. This is achieved by learning a probablistic latent space with a novel view-specific diversity loss. Thorough quantitative and qualitative analysis is performed to highlight the significance of the proposed approach. We outperform state-of-the-art approaches on the task of single-view 3D reconstruction on both real and synthetic datasets while generating multiple plausible reconstructions, demonstrating the generalizability and utility of our approach.

Connected through hyperedge HE-1 (induced by source paper "deep sketch based modeling of man made shapes"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to infer and execute 3d shape programs"
    - "3dn 3d deformation network"
    - "deep parametric shape predictions using distance fields"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

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"
    - "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"
    - "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"
    - "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-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"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "towards deeper understanding of variational autoencoding models"

Connected through hyperedge HE-5 (induced by source paper "structure attentioned memory network for monocular depth estimation"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "unsupervised learning of depth and ego motion from video"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "pix2vox context aware 3d reconstruction from single and multi view images"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "realpoint3d point cloud generation from a single image with complex background"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"

Connected through hyperedge HE-7 (induced by source paper "humanmeshnet polygonal mesh recovery of humans"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "3d hand shape and pose from images in the wild"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "monocular total capture posing face body and hands in the wild"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "silhouette guided point cloud reconstruction beyond occlusion"):
  with members:
    - "what do single view 3d reconstruction networks learn"
    - "dense 3d object reconstruction from a single depth view"
    - "boundary cues for 3d object shape recovery"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "soccer on your tabletop"
    - "pcn point completion network"
    - "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"

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)