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 "regret minimization for reinforcement learning by evaluating the optimal bias function"):
  Abstract: We present an algorithm based on the Optimism in the Face of Uncertainty (OFU) principle which is able to learn Reinforcement Learning (RL) modeled by Markov decision process (MDP) with finite state-action space efficiently. By evaluating the state-pair difference of the optimal bias function $h^{*}$, the proposed algorithm achieves a regret bound of $\tilde{O}(\sqrt{SAHT})$for MDP with $S$ states and $A$ actions, in the case that an upper bound $H$ on the span of $h^{*}$, i.e., $sp(h^{*})$ is known. This result outperforms the best previous regret bounds $\tilde{O}(HS\sqrt{AT})$ [Bartlett and Tewari, 2009] by a factor of $\sqrt{SH}$. Furthermore, this regret bound matches the lower bound of $\Omega(\sqrt{SAHT})$ [Jaksch et al., 2010] up to a logarithmic factor. As a consequence, we show that there is a near optimal regret bound of $\tilde{O}(\sqrt{SADT})$ for MDPs with finite diameter $D$ compared to the lower bound of $\Omega(\sqrt{SADT})$ [Jaksch et al., 2010].

Includes members:
  Member "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "efficient bias span constrained exploration exploitation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "variance reduction methods for sublinear reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "posterior sampling for large scale reinforcement learning":
    also connected through other hyperedges:
      (none)
  Member "learning unknown markov decision processes a thompson sampling approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "pac bounds for discounted mdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "agnostic q learning with function approximation in deterministic systems tight bounds on approximation error and sample complexity")
  Member "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)