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 "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"):
  Abstract: To assure that an autonomous car is driving safely on public roads, its deep learning-based object detector should not only predict correctly, but show its prediction confidence as well. In this work, we present practical methods to capture uncertainties in object detection for autonomous driving. We propose a probabilistic 3D vehicle detector for Lidar point clouds that can model both classification and spatial uncertainty. Experimental results show that our method captures reliable uncertainties related to the detection accuracy, vehicle distance and occlusion. The results also show that we can improve the detection performance by 1%-5% by modeling the uncertainty.

Includes members:
  Member "pixor real time 3d object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review deep learning on 3d point clouds")
  Member "a general pipeline for 3d detection of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network")
  Member "optimal sensor data fusion architecture for object detection in adverse weather conditions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multimodal end to end autonomous driving")
  Member "joint 3d proposal generation and object detection from view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "voxelnet end to end learning for point cloud based 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "fast lidar based road detection using fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "relation networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "proposal learning for semi supervised object detection")
  Member "frustum pointnets for 3d object detection from rgb d data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "review deep learning on 3d point clouds")

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.RO(Robotics)