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: "meshnet mesh neural network for 3d shape representation"
  Abstract: Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics. Regarding the task of 3D shape representation, there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid, multi-view and point cloud. However, there is little effort on using mesh data in recent years, due to the complexity and irregularity of mesh data. In this paper, we propose a mesh neural network, named MeshNet, to learn 3D shape representation from mesh data. In this method, face-unit and feature splitting are introduced, and a general architecture with available and effective blocks are proposed. In this way, MeshNet is able to solve the complexity and irregularity problem of mesh and conduct 3D shape representation well. We have applied the proposed MeshNet method in the applications of 3D shape classification and retrieval. Experimental results and comparisons with the state-of-the-art methods demonstrate that the proposed MeshNet can achieve satisfying 3D shape classification and retrieval performance, which indicates the effectiveness of the proposed method on 3D shape representation.

Connected through hyperedge HE-1 (induced by source paper "pointhop a lightweight learning model on point sets for 3d classification"):
  with members:
    - "pointhop an explainable machine learning method for point cloud classification"
    - "interpretable convolutional neural networks via feedforward design"
    - "dynamic graph cnn for learning on point clouds"
    - "on data driven saak transform"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet point cloud auto encoder via deep grid deformation"

Connected through hyperedge HE-2 (induced by source paper "neural pose transfer by spatially adaptive instance normalization"):
  with members:
    - "multi garment net learning to dress 3d people from images"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "multimodal unsupervised image to image translation"
    - "3d coded 3d correspondences by deep deformation"
    - "which training methods for gans do actually converge"
    - "variational autoencoders for deforming 3d mesh models"
    - "modulating early visual processing by language"

Connected through hyperedge HE-3 (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"
    - "dynamic graph cnn for learning on point clouds"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "on data driven saak transform"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "beyond top grasps through scene completion"):
  with members:
    - "deep dexterous grasping of novel objects from a single view"
    - "robust grasp planning over uncertain shape completions"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "multi modal geometric learning for grasping and manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

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)