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: "image2mesh a learning framework for single image 3d reconstruction"
  Abstract: One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks. Recent works have relied on volumetric or point cloud representations, but such approaches suffer from a number of issues such as computational complexity, unordered data, and lack of finer geometry. This paper demonstrates that a mesh representation (i.e. vertices and faces to form polygonal surfaces) is able to capture fine-grained geometry for 3D reconstruction tasks. A mesh however is also unstructured data similar to point clouds. We address this problem by proposing a learning framework to infer the parameters of a compact mesh representation rather than learning from the mesh itself. This compact representation encodes a mesh using free-form deformation and a sparse linear combination of models allowing us to reconstruct 3D meshes from single images. In contrast to prior work, we do not rely on silhouettes and landmarks to perform 3D reconstruction. We evaluate our method on synthetic and real-world datasets with very promising results. Our framework efficiently reconstructs 3D objects in a low-dimensional way while preserving its important geometrical aspects.

Connected through hyperedge HE-1 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "3dn 3d deformation network"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "3dn 3d deformation network"):
  with members:
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-4 (induced by source paper "differentiable surface splatting for point based geometry processing"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "inverse path tracing for joint material and lighting estimation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "3d aware scene manipulation via inverse graphics"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-5 (induced by source paper "deep mesh reconstruction from single rgb images via topology modification networks"):
  with members:
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "learning category specific mesh reconstruction from image collections"
    - "bodynet volumetric inference of 3d human body shapes"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "siclope silhouette based clothed people"

Connected through hyperedge HE-6 (induced by source paper "not only look but observe variational observation model of scene level 3d multi object understanding for probabilistic slam"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "toward predictive machine learning for active vision"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "a variational feature encoding method of 3d object for probabilistic semantic slam"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "3d object reconstruction from a single depth view with adversarial learning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-7 (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"
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "the perception distortion tradeoff"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-8 (induced by source paper "deeporgannet on the fly reconstruction and visualization of 3d 4d lung models from single view projections by deep deformation network"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformnet free form deformation network for 3d shape reconstruction from a single image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single image"

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)