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 "stochastic regret minimization in extensive form games"):
  Abstract: Monte-Carlo counterfactual regret minimization (MCCFR) is the state-of-the-art algorithm for solving sequential games that are too large for full tree traversals. It works by using gradient estimates that can be computed via sampling. However, stochastic methods for sequential games have not been investigated extensively beyond MCCFR. In this paper we develop a new framework for developing stochastic regret minimization methods. This framework allows us to use any regret-minimization algorithm, coupled with any gradient estimator. The MCCFR algorithm can be analyzed as a special case of our framework, and this analysis leads to significantly-stronger theoretical on convergence, while simultaneously yielding a simplified proof. Our framework allows us to instantiate several new stochastic methods for solving sequential games. We show extensive experiments on three games, where some variants of our methods outperform MCCFR.

Includes members:
  Member "a modern introduction to online learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps")
  Member "correlation in extensive form games saddle point formulation and benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning")
  Member "regret circuits composability of regret minimizers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combining no regret and q learning")
  Member "solving imperfect information games via discounted regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combining no regret and q learning")
  Member "online convex optimization for sequential decision processes and extensive form games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to correlate in multi player general sum sequential games")
  Member "robust stackelberg equilibria in extensive form games and extension to limited lookahead":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "limited lookahead in imperfect information games")
  Member "deepstack expert level artificial intelligence in heads up no limit poker":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")

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.GT(Computer Science and Game Theory)