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: "collaborative planning for mixed autonomy lane merging"
  Abstract: Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is controlled by a selfishness factor. We test our approach on a simulated two-lane highway where the AV and HV merge into each other's lanes. In a user study with 21 subjects and 6 different selfishness factors, we found that our planning approach was sound and that both agents had less merging times when a factor that balances the rewards for the two agents was chosen. Our results on double lane merging suggest it to be a non-zero-sum game and encourage further investigation on collaborative decision making algorithms for mixed-autonomy traffic.

Connected through hyperedge HE-1 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments"):
  with members:
    - "search based optimal motion planning for automated driving"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "carla an open urban driving simulator"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"

Connected through hyperedge HE-2 (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"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "prioritized experience replay"

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.AI(Artificial Intelligence)