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: "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
  Abstract: 3D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance. Among them, point cloud and multi-view based 3D shape representations are promising recently, and their corresponding deep models have shown significant performance on 3D shape recognition. However, there is little effort concentrating point cloud data and multi-view data for 3D shape representation, which is, in our consideration, beneficial and compensated to each other. In this paper, we propose the Point-View Network (PVNet), the first framework integrating both the point cloud and the multi-view data towards joint 3D shape recognition. More specifically, an embedding attention fusion scheme is proposed that could employ high-level features from the multi-view data to model the intrinsic correlation and discriminability of different structure features from the point cloud data. In particular, the discriminative descriptions are quantified and leveraged as the soft attention mask to further refine the structure feature of the 3D shape. We have evaluated the proposed method on the ModelNet40 dataset for 3D shape classification and retrieval tasks. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework can achieve superior performance.

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"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "cbam convolutional block attention module"
    - "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 "point attention network for semantic segmentation of 3d point clouds"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "sbnet sparse blocks network for fast inference"
    - "splatnet sparse lattice networks for point cloud processing"
    - "local spectral graph convolution for point set feature learning"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"

Connected through hyperedge HE-3 (induced by source paper "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"):
  with members:
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "pixor real time 3d object detection from point clouds"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "3d point capsule networks"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"

Connected through hyperedge HE-4 (induced by source paper "point2spatialcapsule aggregating features and spatial relationships of local regions on point clouds using spatial aware capsules"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "associatively segmenting instances and semantics in point clouds"
    - "relation shape convolutional neural network for point cloud analysis"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"

Connected through hyperedge HE-5 (induced by source paper "revisiting point cloud classification a new benchmark dataset and classification model on real world data"):
  with members:
    - "flex convolution million scale point cloud learning beyond grid worlds"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "local spectral graph convolution for point set feature learning"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "deep sets"
    - "learning to drive from simulation without real world labels"

Connected through hyperedge HE-6 (induced by source paper "equivariant multi view networks"):
  with members:
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "a deeper look at 3d shape classifiers"
    - "3d g cnns for pulmonary nodule detection"
    - "cubenet equivariance to 3d rotation and translation"
    - "tensor field networks rotation and translation equivariant neural networks for 3d point clouds"
    - "a general theory of equivariant cnns on homogeneous spaces"
    - "spnet deep 3d object classification and retrieval using stereographic projection"
    - "polar transformer networks"

Connected through hyperedge HE-7 (induced by source paper "pointhop an explainable machine learning method for point cloud classification"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "ensembles of feedforward designed convolutional neural networks"
    - "interpretable convolutional neural networks via feedforward design"
    - "pointpillars fast encoders for object detection from point clouds"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "on data driven saak transform"

Connected through hyperedge HE-8 (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"
    - "pvrnet point view relation neural network for 3d shape recognition"
    - "meshnet mesh neural network for 3d shape representation"
    - "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)