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: "inverse reinforcement learning with simultaneous estimation of rewards and dynamics"
  Abstract: Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current IRL approaches assume that if the transition model is unknown, additional samples from the system's dynamics are accessible, or the observed behavior provides enough samples of the system's dynamics to solve the inverse problem accurately. These assumptions are often not satisfied. To overcome this, we present a gradient-based IRL approach that simultaneously estimates the system's dynamics. By solving the combined optimization problem, our approach takes into account the bias of the demonstrations, which stems from the generating policy. The evaluation on a synthetic MDP and a transfer learning task shows improvements regarding the sample efficiency as well as the accuracy of the estimated reward functions and transition models.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-2 (induced by source paper "how you act tells a lot privacy leakage attack on deep reinforcement learning"):
  with members:
    - "risk averse robust adversarial reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "deep reinforcement learning that matters"
    - "virtual to real reinforcement learning for autonomous driving"
    - "attention is all you need"
    - "robust adversarial reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "building second order mental models for human robot interaction"):
  with members:
    - "on the utility of model learning in hri"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "modelling user s theory of ai s mind in interactive intelligent systems"
    - "machine theory of mind"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "towards efficient detection and optimal response against sophisticated opponents"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"

Connected through hyperedge HE-4 (induced by source paper "truly batch apprenticeship learning with deep successor features"):
  with members:
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "successor features for transfer in reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-5 (induced by source paper "inverse rational control with partially observable continuous nonlinear dynamics"):
  with members:
    - "inverse pomdp inferring what you think from what you do"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "interactive learning of environment dynamics for sequential tasks"):
  with members:
    - "value iteration networks"
    - "apprenticeship learning using inverse reinforcement learning and gradient methods"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-7 (induced by source paper "inverse rational control inferring what you think from how you forage"):
  with members:
    - "where do you think you re going inferring beliefs about dynamics from behavior"

Connected through hyperedge HE-8 (induced by source paper "where do you think you re going inferring beliefs about dynamics from behavior"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "shared autonomy via deep reinforcement learning"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "impossibility of deducing preferences and rationality from human policy"
    - "reinforcement learning with deep energy based policies"

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)