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: "incremental model based learners with formal learning time guarantees"
  Abstract: Model-based learning algorithms have been shown to use experience efficiently when learning to solve Markov Decision Processes (MDPs) with finite state and action spaces. However, their high computational cost due to repeatedly solving an internal model inhibits their use in large-scale problems. We propose a method based on real-time dynamic programming (RTDP) to speed up two model-based algorithms, RMAX and MBIE (model-based interval estimation), resulting in computationally much faster algorithms with little loss compared to existing bounds. Specifically, our two new learning algorithms, RTDP-RMAX and RTDP-IE, have considerably smaller computational demands than RMAX and MBIE. We develop a general theoretical framework that allows us to prove that both are efficient learners in a PAC (probably approximately correct) sense. We also present an experimental evaluation of these new algorithms that helps quantify the tradeoff between computational and experience demands.

Connected through hyperedge HE-1 (induced by source paper "temple learning template of transitions for sample efficient multi task rl"):
  with members:
    - "an optimal online method of selecting source policies for reinforcement learning"
    - "sample complexity of multi task reinforcement learning"
    - "clustering markov decision processes for continual transfer"

Connected through hyperedge HE-2 (induced by source paper "can agents learn by analogy an inferable model for pac reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "sample optimal parametric q learning with linear transition models"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning in rich observation mdps using spectral methods"

Connected through hyperedge HE-3 (induced by source paper "tight regret bounds for model based reinforcement learning with greedy policies"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "learning latent dynamics for planning from pixels"
    - "integrating planning for task completion dialogue policy learning"
    - "efficient bias span constrained exploration exploitation in reinforcement learning"
    - "is q learning provably efficient"
    - "policy certificates towards accountable reinforcement learning"
    - "how to combine tree search methods in reinforcement learning"
    - "feedback based tree search for reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "model based bayesian exploration"
    - "model based bayesian reinforcement learning in large structured domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "when simple exploration is sample efficient identifying sufficient conditions for random exploration to yield pac rl algorithms"):
  with members:
    - "sample complexity of multi task reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-6 (induced by source paper "a pac rl algorithm for episodic pomdps"):
  with members:
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "pac bounds for discounted mdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"
    - "bounds on sample size for policy evaluation in markov environments"

Connected through hyperedge HE-7 (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"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-8 (induced by source paper "bounded optimal exploration in mdp"):
  with members:
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "near optimal brl using optimistic local transitions"
    - "variance based rewards for approximate bayesian reinforcement learning"
    - "provably bounded optimal agents"

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)