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 hierarchical shape segmentation and labeling from online repositories"
  Abstract: We propose a method for converting geometric shapes into hierarchically segmented parts with part labels. Our key idea is to train category-specific models from the scene graphs and part names that accompany 3D shapes in public repositories. These freely-available annotations represent an enormous, untapped source of information on geometry. However, because the models and corresponding scene graphs are created by a wide range of modelers with different levels of expertise, modeling tools, and objectives, these models have very inconsistent segmentations and hierarchies with sparse and noisy textual tags. Our method involves two analysis steps. First, we perform a joint optimization to simultaneously cluster and label parts in the database while also inferring a canonical tag dictionary and part hierarchy. We then use this labeled data to train a method for hierarchical segmentation and labeling of new 3D shapes. We demonstrate that our method can mine complex information, detecting hierarchies in man-made objects and their constituent parts, obtaining finer scale details than existing alternatives. We also show that, by performing domain transfer using a few supervised examples, our technique outperforms fully-supervised techniques that require hundreds of manually-labeled models.

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"

Connected through hyperedge HE-2 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "learning to infer and execute 3d shape programs"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "grains generative recursive autoencoders for indoor scenes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-3 (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-4 (induced by source paper "hierarchy denoising recursive autoencoders for 3d scene layout prediction"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "birdnet a 3d object detection framework from lidar information"
    - "complex yolo real time 3d object detection on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"):
  with members:
    - "learning to group and label fine grained shape components"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "point convolutional neural networks by extension operators"
    - "grains generative recursive autoencoders for indoor scenes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "scores shape composition with recursive substructure priors"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-6 (induced by source paper "learning point embeddings from shape repositories for few shot segmentation"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "bae net branched autoencoder for shape co segmentation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "tangent convolutions for dense prediction in 3d"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "in defense of the triplet loss for person re identification"
    - "tags2parts discovering semantic regions from shape tags"

Connected through hyperedge HE-7 (induced by source paper "learning to group and label fine grained shape components"):
  with members:
    - "deformation driven shape correspondence via shape recognition"
    - "complementme weakly supervised component suggestions for 3d modeling"
    - "3d shape segmentation with projective convolutional networks"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "shapenet an information rich 3d model repository"
    - "conditional random fields as recurrent neural networks"
    - "fast r cnn"

Connected through hyperedge HE-8 (induced by source paper "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"):
  with members:
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "virtualhome simulating household activities via programs"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "chalet cornell house agent learning environment"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"

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.GR(Graphics)