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: "regret bounds for discounted mdps"
  Abstract: Recently, it has been shown that carefully designed reinforcement learning (RL) algorithms can achieve near-optimal regret in the episodic or the average-reward setting. However, in practice, RL algorithms are applied mostly to the infinite-horizon discounted-reward setting, so it is natural to ask what the lowest regret an algorithm can achieve is in this case, and how close to the optimal the regrets of existing RL algorithms are. In this paper, we prove a regret lower bound of $\Omega\left(\frac{\sqrt{SAT}}{1 - \gamma} - \frac{1}{(1 - \gamma)^2}\right)$ when $T\geq SA$ on any learning algorithm for infinite-horizon discounted Markov decision processes (MDP), where $S$ and $A$ are the numbers of states and actions, $T$ is the number of actions taken, and $\gamma$ is the discounting factor. We also show that a modified version of the double Q-learning algorithm gives a regret upper bound of $\tilde{O}\left(\frac{\sqrt{SAT}}{(1 - \gamma)^{2.5}}\right)$ when $T\geq SA$. Compared to our bounds, previous best lower and upper bounds both have worse dependencies on $T$ and $\gamma$, while our dependencies on $S, A, T$ are optimal. The proof of our upper bound is inspired by recent advances in the analysis of Q-learning in the episodic setting, but the cyclic nature of infinite-horizon MDPs poses many new challenges.

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)