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: "near optimal brl using optimistic local transitions"
  Abstract: Model-based Bayesian Reinforcement Learning (BRL) allows a found formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL suffer from such a combinatorial explosion that a large body of work relies on heuristic algorithms. This paper introduces BOLT, a simple and (almost) deterministic heuristic algorithm for BRL which is optimistic about the transition function. We analyze BOLT's sample complexity, and show that under certain parameters, the algorithm is near-optimal in the Bayesian sense with high probability. Then, experimental results highlight the key differences of this method compared to previous work.

Connected through hyperedge HE-1 (induced by source paper "bayesian reinforcement learning via deep sparse sampling"):
  with members:
    - "multi step greedy and approximate real time dynamic programming"
    - "bayesian reinforcement learning a survey"
    - "learning to optimize via posterior sampling"
    - "monte carlo utility estimates for bayesian reinforcement learning"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "robust bayesian reinforcement learning through tight lower bounds"

Connected through hyperedge HE-2 (induced by source paper "variational bayesian reinforcement learning with regret bounds"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "a short variational proof of equivalence between policy gradients and soft q learning"
    - "equivalence between policy gradients and soft q learning"
    - "the uncertainty bellman equation and exploration"
    - "boltzmann exploration done right"
    - "a distributional perspective on reinforcement learning"
    - "count based exploration with neural density models"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "active reinforcement learning with monte carlo tree search"):
  with members:
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "trial without error towards safe reinforcement learning via human intervention"
    - "thinking fast and slow with deep learning and tree search"
    - "robot planning with mathematical models of human state and action"
    - "generative adversarial imitation learning"
    - "benchmarking for bayesian reinforcement learning"
    - "bayesian reinforcement learning a survey"
    - "algorithms for multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "unifying count based exploration and intrinsic motivation"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "thompson sampling is asymptotically optimal in general environments"
    - "vime variational information maximizing exploration"
    - "pixel recurrent neural networks"
    - "state of the art control of atari games using shallow reinforcement learning"
    - "prioritized experience replay"
    - "increasing the action gap new operators for reinforcement learning"
    - "action conditional video prediction using deep networks in atari games"

Connected through hyperedge HE-5 (induced by source paper "bounded optimal exploration in mdp"):
  with members:
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "incremental model based learners with formal learning time guarantees"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "provably bounded optimal agents"

Connected through hyperedge HE-6 (induced by source paper "bayesian reinforcement learning a survey"):
  with members:
    - "benchmarking for bayesian reinforcement learning"
    - "on the prior sensitivity of thompson sampling"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "model based bayesian exploration"
    - "further optimal regret bounds for thompson sampling"
    - "model based bayesian reinforcement learning in large structured domains"

Connected through hyperedge HE-7 (induced by source paper "incentivizing exploration in reinforcement learning with deep predictive models"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "variance based rewards for approximate bayesian reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
    - "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
    - "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"
    - "decision theoretic coordination and control for active multi camera surveillance in uncertain partially observable environments"
    - "active markov information theoretic path planning for robotic environmental sensing"

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)