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: "pointseg real time semantic segmentation based on 3d lidar point cloud"
  Abstract: In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method for road-objects based on spherical images. We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map. To make PointSeg applicable on a mobile system, we build the model based on the light-weight network, SqueezeNet, with several improvements. It maintains a good balance between memory cost and prediction performance. Our model is trained on spherical images and label masks projected from the KITTI 3D object detection dataset. Experiments show that PointSeg can achieve competitive accuracy with 90fps on a single GPU 1080ti. which makes it quite compatible for autonomous driving applications.

Connected through hyperedge HE-1 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "deepicp an end to end deep neural network for 3d point cloud registration"
    - "key net keypoint detection by handcrafted and learned cnn filters"
    - "lo net deep real time lidar odometry"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "salsanext fast semantic segmentation of lidar point clouds for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "deep learning for 3d point clouds a survey"
    - "salsanet fast road and vehicle segmentation in lidar point clouds for autonomous driving"
    - "fast scnn fast semantic segmentation network"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "splatnet sparse lattice networks for point cloud processing"

Connected through hyperedge HE-3 (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"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "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-4 (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-5 (induced by source paper "fuseseg lidar point cloud segmentation fusing multi modal data"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "pointpillars fast encoders for object detection from point clouds"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-6 (induced by source paper "deeptemporalseg temporally consistent semantic segmentation of 3d lidar scans"):
  with members:
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "deep semantic classification for 3d lidar data"
    - "focal loss for dense object detection"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "lo net deep real time lidar odometry"):
  with members:
    - "deep auxiliary learning for visual localization and odometry"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "imls slam scan to model matching based on 3d data"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "lego learning edge with geometry all at once by watching videos"
    - "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry"
    - "geometric loss functions for camera pose regression with deep learning"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-8 (induced by source paper "salsanet fast road and vehicle segmentation in lidar point clouds for autonomous driving"):
  with members:
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "pixor real time 3d object detection from point clouds"
    - "latte accelerating lidar point cloud annotation via sensor fusion one click annotation and tracking"
    - "fast scnn fast semantic segmentation network"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "boosting lidar based semantic labeling by cross modal training data generation"
    - "voxelnet end to end learning for point cloud based 3d object detection"

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)