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: "safe reinforcement learning with scene decomposition for navigating complex urban environments"
  Abstract: Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rule-based and reinforcement learning (RL) approaches. We first present a safe RL algorithm relying on a model-checker to ensure safety guarantees. To make the decision strategy robust to perception errors and occlusions, we introduce a belief update technique using a learning based approach. Finally, we use a scene decomposition approach to scale our algorithm to environments with multiple traffic participants. We empirically demonstrate that our algorithm outperforms rule-based methods and reinforcement learning techniques on a complex intersection scenario.

Connected through hyperedge HE-1 (induced by source paper "safety considerations in deep control policies with safety barrier certificates under uncertainty"):
  with members:
    - "control barrier functions theory and applications"
    - "barrier certified adaptive reinforcement learning with applications to brushbot navigation"
    - "algorithms for verifying deep neural networks"
    - "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"
    - "temporal logic guided safe reinforcement learning using control barrier functions"
    - "worst cases policy gradients"
    - "end to end safe reinforcement learning through barrier functions for safety critical continuous control tasks"
    - "sampling based motion planning via control barrier functions"

Connected through hyperedge HE-2 (induced by source paper "safety considerations in deep control policies with probabilistic safety barrier certificates"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "worst cases policy gradients"
    - "learning controls using cross modal representations bridging simulation and reality for drone racing"
    - "oil observational imitation learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "reluplex an efficient smt solver for verifying deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "cooperation aware reinforcement learning for merging in dense traffic"):
  with members:
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "collaborative planning for mixed autonomy lane merging"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "prioritized experience replay"

Connected through hyperedge HE-4 (induced by source paper "automatic testing and falsification with dynamically constrained reinforcement learning"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "falsification of cyber physical systems using deep reinforcement learning"
    - "carla an open urban driving simulator"
    - "a temporal logic approach to modular design of synthetic biological circuits"
    - "playing atari with deep reinforcement learning"

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)