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: "belief state planning for autonomously navigating urban intersections"
  Abstract: Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.

Connected through hyperedge HE-1 (induced by source paper "autonomous driving at intersections a critical turning point approach for left turns"):
  with members:
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"

Connected through hyperedge HE-2 (induced by source paper "game theoretic modeling of traffic in unsignalized intersection network for autonomous vehicle control verification and validation"):
  with members:
    - "a game theoretic approach to decision making for multiple vehicles at roundabout"
    - "game theoretic modeling of multi vehicle interactions at uncontrolled intersections"
    - "decision making for autonomous vehicles at unsignalized intersection in presence of malicious vehicles"
    - "using control synthesis to generate corner cases a case study on autonomous driving"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "adaptive game theoretic decision making for autonomous vehicle control at roundabouts"
    - "carla an open urban driving simulator"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "safe reinforcement learning with scene decomposition for navigating complex urban environments"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "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"
    - "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-4 (induced by source paper "partially observable planning and learning for systems with non uniform dynamics"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "qmdp net deep learning for planning under partial observability"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "value iteration networks"
    - "backprop kf learning discriminative deterministic state estimators"
    - "learning to navigate in complex environments"
    - "deep recurrent q learning for partially observable mdps"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "collaborative planning for mixed autonomy lane merging"):
  with members:
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"

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)