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: "the bottleneck simulator a model based deep reinforcement learning approach"
  Abstract: Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bottleneck Simulator: a model-based reinforcement learning method which combines a learned, factorized transition model of the environment with rollout simulations to learn an effective policy from few examples. The learned transition model employs an abstract, discrete (bottleneck) state, which increases sample efficiency by reducing the number of model parameters and by exploiting structural properties of the environment. We provide a mathematical analysis of the Bottleneck Simulator in terms of fixed points of the learned policy, which reveals how performance is affected by four distinct sources of error: an error related to the abstract space structure, an error related to the transition model estimation variance, an error related to the transition model estimation bias, and an error related to the transition model class bias. Finally, we evaluate the Bottleneck Simulator on two natural language processing tasks: a text adventure game and a real-world, complex dialogue response selection task. On both tasks, the Bottleneck Simulator yields excellent performance beating competing approaches.

Connected through hyperedge HE-1 (induced by source paper "combining q learning and search with amortized value estimates"):
  with members:
    - "action guidance with mcts for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "policy gradient search online planning and expert iteration without search trees"
    - "benchmarking model based reinforcement learning"
    - "an investigation of model free planning"
    - "unsupervised visuomotor control through distributional planning networks"
    - "structured agents for physical construction"
    - "universal planning networks"

Connected through hyperedge HE-2 (induced by source paper "biased aggregation rollout and enhanced policy improvement for reinforcement learning"):
  with members:
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "value iteration with options and state aggregation"
    - "discretized approximations for pomdp with average cost"
    - "potential based shaping and q value initialization are equivalent"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent deep reinforcement learning with human strategies"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent deep reinforcement learning with extremely noisy observations"
    - "a human mixed strategy approach to deep reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "dual policy iteration"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "zero shot visual imitation"
    - "graph networks as learnable physics engines for inference and control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"

Connected through hyperedge HE-5 (induced by source paper "value propagation networks"):
  with members:
    - "cooperative motion planning for non holonomic agents with value iteration networks"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "learning generalized reactive policies using deep neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "value prediction network"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "generalized value iteration networks life beyond lattices"
    - "neural slam learning to explore with external memory"

Connected through hyperedge HE-6 (induced by source paper "feature based aggregation and deep reinforcement learning a survey and some new implementations"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "deep learning in neural networks"
    - "discretized approximations for pomdp with average cost"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)