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 "argoverse 3d tracking and forecasting with rich maps"):
  Abstract: We present Argoverse -- two datasets designed to support autonomous vehicle machine learning tasks such as 3D tracking and motion forecasting. Argoverse was collected by a fleet of autonomous vehicles in Pittsburgh and Miami. The Argoverse 3D Tracking dataset includes 360 degree images from 7 cameras with overlapping fields of view, 3D point clouds from long range LiDAR, 6-DOF pose, and 3D track annotations. Notably, it is the only modern AV dataset that provides forward-facing stereo imagery. The Argoverse Motion Forecasting dataset includes more than 300,000 5-second tracked scenarios with a particular vehicle identified for trajectory forecasting. Argoverse is the first autonomous vehicle dataset to include "HD maps" with 290 km of mapped lanes with geometric and semantic metadata. All data is released under a Creative Commons license at this http URL. In our baseline experiments, we illustrate how detailed map information such as lane direction, driveable area, and ground height improves the accuracy of 3D object tracking and motion forecasting. Our tracking and forecasting experiments represent only an initial exploration of the use of rich maps in robotic perception. We hope that Argoverse will enable the research community to explore these problems in greater depth.

Includes members:
  Member "deep multi sensor lane detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  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 "precog prediction conditioned on goals in visual multi agent settings":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  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")
  Member "convolutional social pooling for vehicle trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential forecasting of 100 000 points")
  Member "flownet3d learning scene flow in 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "pointpillars fast encoders for object detection from point clouds":
    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.CV(Computer Vision and Pattern Recognition)