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: "pvrnet point view relation neural network for 3d shape recognition"
  Abstract: Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the point-multi-view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models.

Connected through hyperedge HE-1 (induced by source paper "manet multimodal attention network based point view fusion for 3d shape recognition"):
  with members:
    - "gapnet graph attention based point neural network for exploiting local feature of point cloud"
    - "cbam convolutional block attention module"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric deep learning going beyond euclidean data"
    - "residual attention network for image classification"

Connected through hyperedge HE-2 (induced by source paper "relation module for non answerable prediction on question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "compositional attention networks for machine reasoning"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "mv c3d a spatial correlated multi view 3d convolutional neural networks"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "modeling local geometric structure of 3d point clouds using geo cnn"
    - "so net self organizing network for point cloud analysis"
    - "angular triplet center loss for multi view 3d shape retrieval"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (induced by source paper "hrge net hierarchical relational graph embedding network for multi view 3d shape recognition"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "a deeper look at 3d shape classifiers"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "veram view enhanced recurrent attention model for 3d shape classification"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "relation networks for object detection"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "pointdan a multi scale 3d domain adaption network for point cloud representation"):
  with members:
    - "semantic transferable weakly supervised endoscopic lesions segmentation"
    - "domain adaptation for vehicle detection from bird s eye view lidar point cloud data"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "image super resolution using very deep residual channel attention networks"
    - "meshnet mesh neural network for 3d shape representation"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

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)