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 "low latency high level data sharing for connected and autonomous vehicular networks"):
  Abstract: Autonomous vehicles can combine their own data with that of other vehicles to enhance their perceptive ability, and thus improve detection accuracy and driving safety. Data sharing among autonomous vehicles, however, is a challenging problem due to the sheer volume of data generated by various types of sensors on the vehicles. In this paper, we propose a low-latency, high-level (L3) data sharing protocol for connected and autonomous vehicular networks. Based on the L3 protocol, sensing results generated by individual vehicles will be broadcasted simultaneously within a limited sensing zone. The L3 protocol reduces the networking latency by taking advantage of the capture effect of the wireless transmissions occurred among vehicles. With the proposed design principles, we implement and test the L3 protocol in a simulated environment. Simulation results demonstrate that the L3 protocol is able to achieve reliable and fast data sharing among autonomous vehicles.

Includes members:
  Member "cooper cooperative perception for connected autonomous vehicles based on 3d point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "f cooper feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds")
  Member "voxelnet end to end learning for point cloud based 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review deep learning on 3d point clouds")
  Member "frustum pointnets for 3d object detection from rgb d data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review deep learning on 3d point clouds")
  Member "pointfusion deep sensor fusion for 3d bounding box estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "multi view 3d object detection network 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.NI(Networking and Internet Architecture)