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: "model based bayesian exploration"
  Abstract: Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information - the expected improvement in future decision quality arising from the information acquired by exploration. Estimating this quantity requires an assessment of the agent's uncertainty about its current value estimates for states. In this paper we investigate ways of representing and reasoning about this uncertainty in algorithms where the system attempts to learn a model of its environment. We explicitly represent uncertainty about the parameters of the model and build probability distributions over Q-values based on these. These distributions are used to compute a myopic approximation to the value of information for each action and hence to select the action that best balances exploration and exploitation.

Connected through hyperedge HE-1 (induced by source paper "benchmarking model based reinforcement learning"):
  with members:
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-2 (induced by source paper "deep model based reinforcement learning via estimated uncertainty and conservative policy optimization"):
  with members:
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "the uncertainty bellman equation and exploration"
    - "count based exploration with neural density models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-3 (induced by source paper "hyper parameter tuning under a budget constraint"):
  with members:
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "hyperparameter optimization a spectral approach"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "population based training of neural networks"
    - "neural optimizer search with reinforcement learning"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "fast bayesian optimization of machine learning hyperparameters on large datasets"
    - "non stochastic best arm identification and hyperparameter optimization"

Connected through hyperedge HE-4 (induced by source paper "learning montezuma s revenge from a single demonstration"):
  with members:
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "expert augmented actor critic for vizdoom and montezumas revenge"
    - "observe and look further achieving consistent performance on atari"
    - "equivalence between policy gradients and soft q learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-5 (induced by source paper "evolved policy gradients"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "equivalence between policy gradients and soft q learning"
    - "one shot visual imitation learning via meta learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "count based exploration with neural density models"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"

Connected through hyperedge HE-6 (induced by source paper "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"):
  with members:
    - "analysis of agent expertise in ms pac man using value of information based policies"
    - "an analysis of the value of information when exploring stochastic discrete multi armed bandits"
    - "partitioning relational matrices of similarities or dissimilarities using the value of information"
    - "incremental model based learners with formal learning time guarantees"
    - "model based bayesian reinforcement learning in large structured domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "assumed density filtering q learning"):
  with members:
    - "efficient exploration through bayesian deep q networks"
    - "the uncertainty bellman equation and exploration"
    - "deep exploration via bootstrapped dqn"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"
    - "bayesian reinforcement learning a survey"
    - "tractable inference for complex stochastic processes"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "efficient exploration with double uncertain value networks"):
  with members:
    - "parameter space noise for exploration"
    - "the uncertainty bellman equation and exploration"
    - "a distributional perspective on reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "vime variational information maximizing exploration"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "parametric return density estimation for reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"

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)