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: "interaction aware decision making with adaptive strategies under merging scenarios"
  Abstract: In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of cooperativeness. Whether the vehicle is on the merge-lane or main-lane will also influence the driving maneuvers since drivers will behave differently when they have the right-of-way than otherwise. Many traditional methods have been proposed to solve decision making problems under merging scenarios. However, these works either are incapable of modeling complicated interactions or require implementing hand-designed rules which cannot properly handle the uncertainties in real-world scenarios. In this paper, we proposed an interaction-aware decision making with adaptive strategies (IDAS) approach that can let the autonomous vehicle negotiate the road with other drivers by leveraging their cooperativeness under merging scenarios. A single policy is learned under the multi-agent reinforcement learning (MARL) setting via the curriculum learning strategy, which enables the agent to automatically infer other drivers' various behaviors and make decisions strategically. A masking mechanism is also proposed to prevent the agent from exploring states that violate common sense of human judgment and increase the learning efficiency. An exemplar merging scenario was used to implement and examine the proposed method.

Connected through hyperedge HE-1 (induced by source paper "driving in dense traffic with model free reinforcement learning"):
  with members:
    - "cooperation aware lane change control in dense traffic"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"
    - "learning agile and dynamic motor skills for legged robots"
    - "search based optimal motion planning for automated driving"
    - "an introduction to deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "cooperation aware reinforcement learning for merging in dense traffic"):
  with members:
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "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"

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.LG(Machine Learning)