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: "large scale 3d shape reconstruction and segmentation from shapenet core55"
  Abstract: We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation of 3D shapes and 3D reconstruction from single view images. Ten teams have participated in the challenge and the best performing teams have outperformed state-of-the-art approaches on both tasks. A few novel deep learning architectures have been proposed on various 3D representations on both tasks. We report the techniques used by each team and the corresponding performances. In addition, we summarize the major discoveries from the reported results and possible trends for the future work in the field.

Connected through hyperedge HE-1 (induced by source paper "instant recovery of shape from spectrum via latent space connections"):
  with members:
    - "operatornet recovering 3d shapes from difference operators"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometric disentanglement for generative latent shape models"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "sdm net deep generative network for structured deformable mesh"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised deep learning for structured shape matching"
    - "learning implicit fields for generative shape modeling"

Connected through hyperedge HE-2 (induced by source paper "efficient semantic scene completion network with spatial group convolution"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "inference learning and attention mechanisms that exploit and preserve sparsity in convolutional networks"
    - "3dcnn dqn rnn a deep reinforcement learning framework for semantic parsing of large scale 3d point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "octnetfusion learning depth fusion from data"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-3 (induced by source paper "what do single view 3d reconstruction networks learn"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "mortonnet self supervised learning of local features in 3d point clouds"):
  with members:
    - "unsupervised representation learning by predicting image rotations"
    - "dynamic graph cnn for learning on point clouds"
    - "tracking emerges by colorizing videos"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-5 (induced by source paper "pix3d dataset and methods for single image 3d shape modeling"):
  with members:
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "hierarchical surface prediction for 3d object reconstruction"
    - "weakly supervised 3d reconstruction with adversarial constraint"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "learning 3d object categories by looking around them"
    - "semantic scene completion from a single depth image"
    - "rgbd datasets past present and future"

Connected through hyperedge HE-6 (induced by source paper "classification of point cloud scenes with multiscale voxel deep network"):
  with members:
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "structure aware shape synthesis"):
  with members:
    - "3d interpreter networks for viewer centered wireframe modeling"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning a predictable and generative vector representation for objects"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "3d menagerie modeling the 3d shape and pose of animals"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"
    - "single image 3d interpreter network"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"

Connected through hyperedge HE-8 (induced by source paper "im2avatar colorful 3d reconstruction from a single image"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "marrnet 3d shape reconstruction via 2 5d sketches"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "shapenet an information rich 3d model repository"
    - "multi view 3d models from single images with a convolutional network"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

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)