human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "rgb and lidar fusion based 3d semantic segmentation for autonomous driving"):
  Abstract: LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB image to a polar-grid mapping representation used for LiDAR and design early and mid-level fusion architectures. Additionally, we design a hybrid fusion architecture that combines both fusion algorithms. We evaluate our algorithm on KITTI dataset which provides segmentation annotation for cars, pedestrians and cyclists. We evaluate two state-of-the-art architectures namely SqueezeSeg and PointSeg and improve the mIoU score by 10 % in both cases relative to the LiDAR only baseline.

Includes members:
  Member "rtseg real time semantic segmentation comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trained trajectory based automated parking system using visual slam")
  Member "pointseg real time semantic segmentation based on 3d lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cae lo lidar odometry leveraging fully unsupervised convolutional auto encoder for interest point detection and feature description")
  Member "voxelnet end to end learning for point cloud based 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review deep learning on 3d point clouds")
  Member "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

Question: Please tell me which class the source paper 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)