human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "posterior sampling for large scale reinforcement learning"):
  Abstract: Posterior sampling for reinforcement learning (PSRL) is a popular algorithm for learning to control an unknown Markov decision process (MDP). PSRL maintains a distribution over MDP parameters and in an episodic fashion samples MDP parameters, computes the optimal policy for them and executes it. A special case of PSRL is where at the end of each episode the MDP resets to the initial state distribution. Extensions of this idea to general MDPs without state resetting has so far produced non-practical algorithms and in some cases buggy theoretical analysis. This is due to the difficulty of analyzing regret under episode switching schedules that depend on random variables of the true underlying model. We propose a solution to this problem that involves using a deterministic, model-independent episode switching schedule, and establish a Bayes regret bound under mild assumptions. Our algorithm termed deterministic schedule PSRL (DS-PSRL) is efficient in terms of time, sample, and space complexity. Our result is more generally applicable to continuous state action problems. We demonstrate how this algorithm is well suited for sequential recommendation problems such as points of interest (POI). We derive a general procedure for parameterizing the underlying MDPs, to create action condition dynamics from passive data, that do not contain actions. We prove that such parameterization satisfies the assumptions of our analysis.

Includes members:
  Member "learning based control of unknown linear systems with thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "posterior sampling for reinforcement learning worst case regret bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning near optimal policies with low inherent bellman error")

Question: Please tell me which class the source paper 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)