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: "cooperation aware reinforcement learning for merging in dense traffic"
  Abstract: Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving objects will cause the vehicle to freeze and fail the maneuver. Human drivers leverage the cooperation of other drivers to avoid such deadlock situations and convince others to change their behavior. Decision making algorithms must reason about the interaction with other drivers and anticipate a broad range of driver behaviors. In this work, we present a reinforcement learning approach to learn how to interact with drivers with different cooperation levels. We enhanced the performance of traditional reinforcement learning algorithms by maintaining a belief over the level of cooperation of other drivers. We show that our agent successfully learns how to navigate a dense merging scenario with less deadlocks than with online planning methods.

Connected through hyperedge HE-1 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "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 "mixed strategies for robust optimization of unknown objectives"):
  with members:
    - "bayesian optimisation under uncertain inputs"
    - "no regret learning in unknown games with correlated payoffs"
    - "safe intersection management for mixed transportation systems with human driven and autonomous vehicles"
    - "distributionally robust submodular maximization"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "practical bayesian optimization in the presence of outliers"
    - "robust optimization for non convex objectives"
    - "on kernelized multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "inference based strategy alignment for general sum differential games"):
  with members:
    - "on gradient based learning in continuous games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "on the convergence of gradient based learning in continuous games"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"
    - "multimodal probabilistic model based planning for human robot interaction"

Connected through hyperedge HE-4 (induced by source paper "cooperation aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network"):
  with members:
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "real time ecological velocity planning for plug in hybrid vehicles with partial communication to traffic lights"
    - "safe reinforcement learning on autonomous vehicles"
    - "driving in dense traffic with model free reinforcement learning"
    - "design and implementation of ecological adaptive cruise control for autonomous driving with communication to traffic lights"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "explaining how a deep neural network trained with end to end learning steers a car"

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)