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: "dyna style planning with linear function approximation and prioritized sweeping"
  Abstract: We consider the problem of efficiently learning optimal control policies and value functions over large state spaces in an online setting in which estimates must be available after each interaction with the world. This paper develops an explicitly model-based approach extending the Dyna architecture to linear function approximation. Dynastyle planning proceeds by generating imaginary experience from the world model and then applying model-free reinforcement learning algorithms to the imagined state transitions. Our main results are to prove that linear Dyna-style planning converges to a unique solution independent of the generating distribution, under natural conditions. In the policy evaluation setting, we prove that the limit point is the least-squares (LSTD) solution. An implication of our results is that prioritized-sweeping can be soundly extended to the linear approximation case, backing up to preceding features rather than to preceding states. We introduce two versions of prioritized sweeping with linear Dyna and briefly illustrate their performance empirically on the Mountain Car and Boyan Chain problems.

Connected through hyperedge HE-1 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks"):
  with members:
    - "planning with goal conditioned policies"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "universal planning networks"
    - "deepmind control suite"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "value propagation networks"
    - "learning multi level hierarchies with hindsight"

Connected through hyperedge HE-2 (induced by source paper "policy aware model learning for policy gradient methods"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "gradient aware model based policy search"
    - "global optimality guarantees for policy gradient methods"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"

Connected through hyperedge HE-3 (induced by source paper "frequency based search control in dyna"):
  with members:
    - "hill climbing on value estimates for search control in dyna"
    - "a new perspective on machine learning how to do perfect supervised learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "recurrent world models facilitate policy evolution"
    - "value prediction network"
    - "the predictron end to end learning and planning"
    - "continuous deep q learning with model based acceleration"

Connected through hyperedge HE-4 (induced by source paper "meta learning of sequential strategies"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "causal reasoning from meta reinforcement learning"
    - "conditional neural processes"
    - "sample efficient adaptive text to speech"
    - "modeling friends and foes"

Connected through hyperedge HE-5 (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-6 (induced by source paper "model based lookahead reinforcement learning"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "model ensemble trust region policy optimization"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "model based value estimation for efficient model free reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "model based reinforcement learning via meta policy optimization"
    - "value prediction network"

Connected through hyperedge HE-7 (induced by source paper "combating the compounding error problem with a multi step model"):
  with members:
    - "deepmdp learning continuous latent space models for representation learning"
    - "model ensemble trust region policy optimization"
    - "model based active exploration"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "multi step reinforcement learning a unifying algorithm"

Connected through hyperedge HE-8 (induced by source paper "dualdice behavior agnostic estimation of discounted stationary distribution corrections"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "learning dexterous in hand manipulation"
    - "neural approaches to conversational ai"
    - "more robust doubly robust off policy evaluation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "stochastic variance reduction methods for policy evaluation"

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)