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: "point cloud oversegmentation with graph structured deep metric learning"
  Abstract: We propose a new supervized learning framework for oversegmenting 3D point clouds into superpoints. We cast this problem as learning deep embeddings of the local geometry and radiometry of 3D points, such that the border of objects presents high contrasts. The embeddings are computed using a lightweight neural network operating on the points' local neighborhood. Finally, we formulate point cloud oversegmentation as a graph partition problem with respect to the learned embeddings. #R##N#This new approach allows us to set a new state-of-the-art in point cloud oversegmentation by a significant margin, on a dense indoor dataset (S3DIS) and a sparse outdoor one (vKITTI). Our best solution requires over five times fewer superpoints to reach similar performance than previously published methods on S3DIS. Furthermore, we show that our framework can be used to improve superpoint-based semantic segmentation algorithms, setting a new state-of-the-art for this task as well.

Connected through hyperedge HE-1 (induced by source paper "grid gcn for fast and scalable point cloud learning"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "hplflownet hierarchical permutohedral lattice flownet for scene flow estimation on large scale point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "modeling point clouds with self attention and gumbel subset sampling"
    - "point voxel cnn for efficient 3d deep learning"
    - "shellnet efficient point cloud convolutional neural networks using concentric shells statistics"
    - "relation shape convolutional neural network for point cloud analysis"

Connected through hyperedge HE-2 (induced by source paper "a review of point cloud semantic segmentation"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "nonlocal compressive sensing based sar tomography"
    - "associatively segmenting instances and semantics in point clouds"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-3 (induced by source paper "advpc transferable adversarial perturbations on 3d point clouds"):
  with members:
    - "meshadv adversarial meshes for visual recognition"
    - "cross domain transferability of adversarial perturbations"
    - "sada semantic adversarial diagnostic attacks for autonomous applications"
    - "generating 3d adversarial point clouds"
    - "pointcloud saliency maps"
    - "dynamic graph cnn for learning on point clouds"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "pu net point cloud upsampling network"

Connected through hyperedge HE-4 (induced by source paper "dilated point convolutions on the receptive field size of point convolutions on 3d point clouds"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "so net self organizing network for point cloud analysis"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "voxelnet end to end learning for point cloud based 3d object detection"

Connected through hyperedge HE-5 (induced by source paper "lu net an efficient network for 3d lidar point cloud semantic segmentation based on end to end learned 3d features and u net"):
  with members:
    - "riu net embarrassingly simple semantic segmentation of 3d lidar point cloud"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "focal loss for dense object detection"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "riu net embarrassingly simple semantic segmentation of 3d lidar point cloud"):
  with members:
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "squeeze and excitation networks"
    - "icnet for real time semantic segmentation on high resolution images"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "dilated point convolutions on the receptive field of point convolutions"):
  with members:
    - "3d bird s eye view instance segmentation"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "exploring spatial context for 3d semantic segmentation of point clouds"

Connected through hyperedge HE-8 (induced by source paper "supervized segmentation with graph structured deep metric learning"):
  with members:
    - "group normalization"
    - "superpixel sampling networks"
    - "know what your neighbors do 3d semantic segmentation of point clouds"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)