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 "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium"):
  Abstract: We develop provably efficient reinforcement learning algorithms for two-player zero-sum Markov games in which the two players simultaneously take actions. To incorporate function approximation, we consider a family of Markov games where the reward function and transition kernel possess a linear structure. Both the offline and online settings of the problems are considered. In the offline setting, we control both players and the goal is to find the Nash Equilibrium efficiently by minimizing the worst-case duality gap. In the online setting, we control a single player to play against an arbitrary opponent and the goal is to minimize the regret. For both settings, we propose an optimistic variant of the least-squares minimax value iteration algorithm. We show that our algorithm is computationally efficient and provably achieves an $\tilde O(\sqrt{d^3 H^3 T})$ upper bound on the duality gap and regret, without requiring additional assumptions on the sampling model. #R##N#We highlight that our setting requires overcoming several new challenges that are absent in Markov decision processes or turn-based Markov games. In particular, to achieve optimism in simultaneous-move Marko games, we construct both upper and lower confidence bounds of the value function, and then compute the optimistic policy by solving a general-sum matrix game with these bounds as the payoff matrices. As finding the Nash Equilibrium of such a general-sum game is computationally hard, our algorithm instead solves for a Coarse Correlated Equilibrium (CCE), which can be obtained efficiently via linear programming. To our best knowledge, such a CCE-based scheme for implementing optimism has not appeared in the literature and might be of interest in its own right.

Includes members:
  Member "provably efficient rl with rich observations via latent state decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "provably efficient reinforcement learning with linear function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "exploration enhanced politex":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "upper confidence primal dual optimization stochastically constrained markov decision processes with adversarial losses and unknown transitions")
  Member "emergent tool use from multi agent autocurricula":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "automatic curriculum learning for deep rl a short survey")
  Member "solving discounted stochastic two player games with near optimal time and sample complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable self play algorithms for competitive reinforcement learning")
  Member "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "sample optimal parametric q learning using linearly additive features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "frequentist regret bounds for randomized least squares value iteration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)