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: "variance based rewards for approximate bayesian reinforcement learning"
  Abstract: The explore{exploit dilemma is one of the central challenges in Reinforcement Learning (RL). Bayesian RL solves the dilemma by providing the agent with information in the form of a prior distribution over environments; however, full Bayesian planning is intractable. Planning with the mean MDP is a common myopic approximation of Bayesian planning. We derive a novel reward bonus that is a function of the posterior distribution over environments, which, when added to the reward in planning with the mean MDP, results in an agent which explores efficiently and effectively. Although our method is similar to existing methods when given an uninformative or unstructured prior, unlike existing methods, our method can exploit structured priors. We prove that our method results in a polynomial sample complexity and empirically demonstrate its advantages in a structured exploration task.

Connected through hyperedge HE-1 (induced by source paper "varibad a very good method for bayes adaptive deep rl via meta learning"):
  with members:
    - "meta reinforcement learning with task embedding and shared policy"
    - "meta learning of sequential strategies"
    - "meta amortized variational inference and learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta reinforcement learning as task inference"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-2 (induced by source paper "explicit explore exploit algorithms in continuous state spaces"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "self supervised exploration via disagreement"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "model based active exploration"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "practical contextual bandits with regression oracles"

Connected through hyperedge HE-3 (induced by source paper "neural predictive belief representations"):
  with members:
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning awareness models"
    - "representation learning with contrastive predictive coding"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to act by predicting the future"
    - "recurrent reinforcement learning a hybrid approach"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "reward estimation for variance reduction in deep reinforcement learning"):
  with members:
    - "variational inverse control with events a general framework for data driven reward definition"
    - "combined reinforcement learning via abstract representations"
    - "learning the reward function for a misspecified model"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning that matters"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "a distributional perspective on reinforcement learning"
    - "model based planning with discrete and continuous actions"

Connected through hyperedge HE-5 (induced by source paper "pomdp lite for robust robot planning under uncertainty"):
  with members:
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "hidden parameter markov decision processes a semiparametric regression approach for discovering latent task parametrizations"
    - "point based pomdp algorithms improved analysis and implementation"
    - "incremental model based learners with formal learning time guarantees"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-6 (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"
    - "near optimal brl using optimistic local transitions"
    - "provably bounded optimal agents"

Connected through hyperedge HE-7 (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-8 (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"
    - "near optimal brl using optimistic local transitions"
    - "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.LG(Machine Learning)