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: "planning to be surprised optimal bayesian exploration in dynamic environments"
  Abstract: To maximize its success, an AGI typically needs to explore its initially unknown world. Is there an optimal way of doing so? Here we derive an affirmative answer for a broad class of environments.

Connected through hyperedge HE-1 (induced by source paper "ready policy one world building through active learning"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "learning to predict without looking ahead world models without forward prediction"
    - "self supervised exploration via disagreement"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "dream to control learning behaviors by latent imagination"
    - "benchmarking model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning through active inference"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "deep active inference as variational policy gradients"
    - "a unified bellman optimality principle combining reward maximization and empowerment"
    - "deep dynamics models for learning dexterous manipulation"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "efficient exploration via state marginal matching"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-3 (induced by source paper "implicit generative modeling for efficient exploration"):
  with members:
    - "self supervised exploration via disagreement"
    - "hypergan a generative model for diverse performant neural networks"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "large scale study of curiosity driven learning"
    - "model based active exploration"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-4 (induced by source paper "smirl surprise minimizing rl in dynamic environments"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "self supervised exploration via disagreement"
    - "provably efficient maximum entropy exploration"
    - "imitating latent policies from observation"
    - "exploration by random network distillation"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "model based active exploration"

Connected through hyperedge HE-5 (induced by source paper "scaling active inference"):
  with members:
    - "deep active inference as variational policy gradients"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "model based reinforcement learning for atari"
    - "bootstrapping the expressivity with model based planning"
    - "bayesian policy selection using active inference"
    - "uncertainty aware model based policy optimization"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-6 (induced by source paper "self supervised exploration via disagreement"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "large scale study of curiosity driven learning"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "unity a general platform for intelligent agents"
    - "diversity is all you need learning skills without a reward function"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "parameter space noise for exploration"
    - "ex2 exploration with exemplar models for deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "tutorial and survey on probabilistic graphical model and variational inference in deep reinforcement learning"):
  with members:
    - "maximum entropy regularized multi goal reinforcement learning"
    - "variational resampling based assessment of deep neural networks under distribution shift"
    - "resampling based assessment of robustness to distribution shift for deep neural networks"
    - "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-8 (induced by source paper "estimating risk and uncertainty in deep reinforcement learning"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "information directed exploration for deep reinforcement learning"
    - "mitigating planner overfitting in model based reinforcement learning"
    - "safe reinforcement learning with model uncertainty estimates"
    - "exploration by distributional reinforcement learning"
    - "implicit quantile networks for distributional 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)