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 deep reinforcement learning driving policy transfer for autonomous vehicles based on robust control"
  Abstract: Although deep reinforcement learning (deep RL) methods have lots of strengths that are favorable if applied to autonomous driving, real deep RL applications in autonomous driving have been slowed down by the modeling gap between the source (training) domain and the target (deployment) domain. Unlike current policy transfer approaches, which generally limit to the usage of uninterpretable neural network representations as the transferred features, we propose to transfer concrete kinematic quantities in autonomous driving. The proposed robust-control-based (RC) generic transfer architecture, which we call RL-RC, incorporates a transferable hierarchical RL trajectory planner and a robust tracking controller based on disturbance observer (DOB). The deep RL policies trained with known nominal dynamics model are transfered directly to the target domain, DOB-based robust tracking control is applied to tackle the modeling gap including the vehicle dynamics errors and the external disturbances such as side forces. We provide simulations validating the capability of the proposed method to achieve zero-shot transfer across multiple driving scenarios such as lane keeping, lane changing and obstacle avoidance.

Connected through hyperedge HE-1 (induced by source paper "automated lane change strategy using proximal policy optimization based deep reinforcement learning"):
  with members:
    - "quadratic q network for learning continuous control for autonomous vehicles"
    - "continuous control for automated lane change behavior based on deep deterministic policy gradient algorithm"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"

Connected through hyperedge HE-2 (induced by source paper "an online reinforcement learning approach to quality cost aware task allocation for multi attribute social sensing"):
  with members:
    - "the age of social sensing"
    - "towards reliable online clickbait video detection a content agnostic approach"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "simulation to scaled city zero shot policy transfer for traffic control via autonomous vehicles"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "driving policy transfer via modularity and abstraction"
    - "flow a modular learning framework for autonomy in traffic"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "robust adversarial reinforcement learning"
    - "rllib abstractions for distributed reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

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.LG(Machine Learning)