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 free form deformations for 3d object reconstruction"
  Abstract: Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge. Most existing work addresses this issue by employing voxel-based representations. While these approaches benefit greatly from advances in computer vision by generalizing 2D convolutions to the 3D setting, they also have several considerable drawbacks. The computational complexity of voxel-encodings grows cubically with the resolution thus limiting such representations to low-resolution 3D reconstruction. In an attempt to solve this problem, point cloud representations have been proposed. Although point clouds are more efficient than voxel representations as they only cover surfaces rather than volumes, they do not encode detailed geometric information about relationships between points. In this paper we propose a method to learn free-form deformations (FFD) for the task of 3D reconstruction from a single image. By learning to deform points sampled from a high-quality mesh, our trained model can be used to produce arbitrarily dense point clouds or meshes with fine-grained geometry. We evaluate our proposed framework on both synthetic and real-world data and achieve state-of-the-art results on point-cloud and volumetric metrics. Additionally, we qualitatively demonstrate its applicability to label transferring for 3D semantic segmentation.

Connected through hyperedge HE-1 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns"):
  with members:
    - "3dregnet a deep neural network for 3d point registration"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "coherent point drift networks unsupervised learning of non rigid point set registration"
    - "deep sketch based modeling of man made shapes"
    - "3dn 3d deformation network"
    - "deep closest point learning representations for point cloud registration"
    - "faim a convnet method for unsupervised 3d medical image registration"
    - "deepmapping unsupervised map estimation from multiple point clouds"

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"
    - "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"
    - "multi view silhouette and depth decomposition for high resolution 3d object representation"

Connected through hyperedge HE-3 (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"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "megadepth learning single view depth prediction from internet photos"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-4 (induced by source paper "pq net a generative part seq2seq network for 3d shapes"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "sdm net deep generative network for structured deformable mesh"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

Connected through hyperedge HE-5 (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"
    - "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"
    - "3d prnn generating shape primitives with recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning part generation and assembly for structure aware shape synthesis"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "structure aware shape synthesis"
    - "learning fuzzy set representations of partial shapes on dual embedding spaces"
    - "learning implicit fields for generative shape modeling"
    - "learning to generate the unseen via part synthesis and composition"

Connected through hyperedge HE-7 (induced by source paper "sdm net deep generative network for structured deformable mesh"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-8 (induced by source paper "learning structural graph layouts and 3d shapes for long span bridges 3d reconstruction"):
  with members:
    - "primitive based 3d building modeling sensor simulation and estimation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "multi view silhouette and depth decomposition for high resolution 3d object representation"
    - "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)