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: "semantic3d net a new large scale point cloud classification benchmark"
  Abstract: This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

Connected through hyperedge HE-1 (induced by source paper "deepsemantichppc hypothesis based planning over uncertain semantic point clouds"):
  with members:
    - "panoptic segmentation"
    - "learn to score efficient 3d scene exploration by predicting view utility"
    - "surface edge explorer see planning next best views directly from 3d observations"
    - "dynamic graph cnn for learning on point clouds"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning ground traversability from simulations"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-2 (induced by source paper "semanticposs a point cloud dataset with large quantity of dynamic instances"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "synthcity a large scale synthetic point cloud"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "splatnet sparse lattice networks for point cloud processing"
    - "pointseg real time semantic segmentation based on 3d lidar point cloud"
    - "paris lille 3d a large and high quality ground truth urban point cloud dataset for automatic segmentation and classification"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "toronto 3d a large scale mobile lidar dataset for semantic segmentation of urban roadways"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "kpconv flexible and deformable convolution for point clouds"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "paris lille 3d a large and high quality ground truth urban point cloud dataset for automatic segmentation and classification"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-4 (induced by source paper "a review on deep learning techniques for 3d sensed data classification"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "context prediction for unsupervised deep learning on point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "splatnet sparse lattice networks for point cloud processing"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "so net self organizing network for point cloud analysis"

Connected through hyperedge HE-5 (induced by source paper "spherical kernel for efficient graph convolution on 3d point clouds"):
  with members:
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "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"
    - "dense 3d face correspondence"
    - "learning to sample"

Connected through hyperedge HE-6 (induced by source paper "randla net efficient semantic segmentation of large scale point clouds"):
  with members:
    - "a cnn annularly convolutional neural networks on point clouds"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "pcan 3d attention map learning using contextual information for point cloud based retrieval"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "pixor real time 3d object detection from point clouds"
    - "octree guided cnn with spherical kernels for 3d point clouds"

Connected through hyperedge HE-7 (induced by source paper "kpconv flexible and deformable convolution for point clouds"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "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"
    - "classification of point cloud scenes with multiscale voxel deep network"

Connected through hyperedge HE-8 (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"
    - "point cloud oversegmentation with graph structured deep metric learning"
    - "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"

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)