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 "canadian adverse driving conditions dataset"):
  Abstract: The Canadian Adverse Driving Conditions (CADC) dataset was collected with the Autonomoose autonomous vehicle platform, based on a modified Lincoln MKZ. The dataset, collected during winter within the Region of Waterloo, Canada, is the first autonomous vehicle dataset that focuses on adverse driving conditions specifically. It contains 7,000 frames collected through a variety of winter weather conditions of annotated data from 8 cameras (Ximea MQ013CG-E2), Lidar (VLP-32C) and a GNSS+INS system (Novatel OEM638). The sensors are time synchronized and calibrated with the intrinsic and extrinsic calibrations included in the dataset. Lidar frame annotations that represent ground truth for 3D object detection and tracking have been provided by Scale AI.

Includes members:
  Member "scalability in perception for autonomous driving waymo open dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "the apolloscape open dataset for autonomous driving and its application":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles")
  Member "a 3d dataset towards autonomous driving in challenging environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nuscenes a multimodal dataset for autonomous driving")
  Member "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d object detection from lidar data using distance depended feature extraction")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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.CV(Computer Vision and Pattern Recognition)