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: "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
  Abstract: Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to support planning and strategic exploration. Inspired by this, we investigate two issues in leveraging model-based RL for sample efficiency. First we investigate how to perform strategic exploration when exact planning is not feasible and empirically show that optimistic Monte Carlo Tree Search outperforms posterior sampling methods. Second we show how to learn simple deterministic models to support fast learning using object representation. We illustrate the benefit of these ideas by introducing a novel algorithm, Strategic Object Oriented Reinforcement Learning (SOORL), that outperforms state-of-the-art algorithms in the game of Pitfall! in less than 50 episodes.

Connected through hyperedge HE-1 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "reasoning about physical interactions with object oriented prediction and planning"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "strategic object oriented reinforcement learning"
    - "stochastic adversarial video prediction"
    - "unsupervised video object segmentation for deep reinforcement learning"
    - "deep object centric representations for generalizable robot learning"
    - "stochastic variational video prediction"
    - "neural expectation maximization"
    - "taking visual motion prediction to new heightfields"

Connected through hyperedge HE-3 (induced by source paper "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"):
  with members:
    - "strategic object oriented reinforcement learning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "explaining explanations an overview of interpretability of machine learning"
    - "eigenoption discovery through the deep successor representation"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"

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)