human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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)

Connected through hyperedge HE-1 (induced by source paper "inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi agent reinforcement learning"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "learning agile and dynamic motor skills for legged robots"
    - "enabling intuitive human robot teaming using augmented reality and gesture control"
    - "cleaning tasks knowledge transfer between heterogeneous robots a deep learning approach"
    - "a comprehensive review of shepherding as a bio inspired swarm robotics guidance approach"
    - "learning attentional communication for multi agent cooperation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"

Question: Please tell me which class the center node 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)