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 "zero shot autonomous vehicle policy transfer from simulation to real world via adversarial learning"):
  Abstract: In this paper, we demonstrate the first successful zero-shot transfer of an autonomous driving policy directly from simulator to a scaled autonomous vehicle under stochastic disturbances. Using adversarial multi-agent reinforcement learning, in which an adversary perturbed both the inputs and outputs of the autonomous vehicle during training, we train an autonomous vehicle to manage a roundabout merge in the presence of an adversary in simulation. At test time, the adversarial policy successfully reproduces the simulated ramp metering behavior and outperforms both a human driving baseline and adversary-free trained policies. Finally, we demonstrate that the addition of adversarial training considerably improves the stability and robustness of policies being transferred to the real world. Supplementary information and videos can be found at: (this https URL)

Includes members:
  Member "demonstration of a time efficient mobility system using a scaled smart city":
    also connected through other hyperedges:
      (none)
  Member "dissipation of stop and go waves via control of autonomous vehicles field experiments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "watch your back backdoor attacks in deep reinforcement learning based autonomous vehicle control systems")
  Member "control of connected and automated vehicles state of the art and future challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "simulation to scaled city zero shot policy transfer for traffic control via autonomous vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flow a modular learning framework for autonomy in traffic")
  Member "flow a modular learning framework for autonomy in traffic":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "watch your back backdoor attacks in deep reinforcement learning based autonomous vehicle control systems")
  Member "expert level control of ramp metering based on multi task deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using reinforcement learning with partial vehicle detection for intelligent traffic signal control")
  Member "robust adversarial reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "rllib abstractions for distributed reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")

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.SY(Systems and Control)