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: "reinforcement learning with probabilistic guarantees for autonomous driving"
  Abstract: Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain environments, but it does not provide any guarantee on the performance of the resulting policy. We propose a generic approach to enforce probabilistic guarantees on an RL agent. An exploration strategy is derived prior to training that constrains the agent to choose among actions that satisfy a desired probabilistic specification expressed with linear temporal logic (LTL). Reducing the search space to policies satisfying the LTL formula helps training and simplifies reward design. This paper outlines a case study of an intersection scenario involving multiple traffic participants. The resulting policy outperforms a rule-based heuristic approach in terms of efficiency while exhibiting strong guarantees on safety.

Connected through hyperedge HE-1 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles"):
  with members:
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "lane change decision making through deep reinforcement learning with rule based constraints"
    - "safe efficient and comfortable velocity control based on reinforcement learning for autonomous driving"
    - "autonomous highway driving using deep reinforcement learning"
    - "improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning"
    - "human like autonomous car following model with deep reinforcement learning"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"
    - "self training autonomous driving agent"

Connected through hyperedge HE-2 (induced by source paper "safe reinforcement learning with scene decomposition for navigating complex urban environments"):
  with members:
    - "learning negotiating behavior between cars in intersections using deep q learning"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "deep variational reinforcement learning for pomdps"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "belief state planning for autonomously navigating urban intersections"
    - "autonomous braking system via deep reinforcement learning"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-3 (induced by source paper "reconnaissance and planning algorithm for constrained mdp"):
  with members:
    - "lyapunov based safe policy optimization for continuous control"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "a lyapunov based approach to safe reinforcement learning"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "constrained policy optimization"
    - "correct by synthesis reinforcement learning with temporal logic constraints"
    - "risk sensitive reinforcement learning applied to control under constraints"

Connected through hyperedge HE-4 (induced by source paper "learning when to drive in intersections by combining reinforcement learning and model predictive control"):
  with members:
    - "real time constrained trajectory planning and vehicle control for proactive autonomous driving with road users"
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learning values across many orders of magnitude"

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.RO(Robotics)