human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "human like decision making for autonomous driving via adversarial inverse reinforcement learning"):
  Abstract: To make human-like decisions under complex driving environment is a challenging task for autonomous agents. Imitation Learning or learning-from-demonstration methods have seen great potential for achieving such a goal. Some state-of-the-art studies apply Generative Adversarial Imitation Learning (GAIL) to learn sequential decision-making and control policies. While GAIL can directly learn a policy, it lacks the ability to recover a reward function, which is considered robust and adaptable to environments changes. Adversarial Inverse Reinforcement Learning (AIRL) is another learning-from-demonstration method that can achieve similar benefits as GAIL but also learns the reward function with the policy simultaneously. In the original work of AIRL, it has been demonstrated in single-agent environments such as maze navigation and ant running tasks in OpenAI Gyms. In this paper, we augment AIRL by concatenating semantic reward terms in the learning framework to improve and stabilize its performance, and then extend it to a more practical but challenging situation, i.e. decision-making scenario in highly interactive driving environment. Four performance evaluation metrics are proposed and compared with some Imitation Learning based methods and Reinforcement Learning based methods. Simulation results show that the augmented AIRL outperforms all the other methods, and the trained vehicle agent can perform decision-making behaviors comparable with that of the experts.

Includes members:
  Member "a reinforcement learning based approach for automated lane change maneuvers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "automated driving maneuvers under interactive environment based on deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial inverse reinforcement learning for decision making in autonomous driving")
  Member "imitating driver behavior with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")
  Member "learning robust rewards with adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "safe multi agent reinforcement learning for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "guided cost learning deep inverse optimal control via policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

Question: Please tell me which class the source paper 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)