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: "solving imperfect information games via discounted regret minimization"
  Abstract: Counterfactual regret minimization (CFR) is a family of iterative algorithms that are the most popular and, in practice, fastest approach to approximately solving large imperfect-information games. In this paper we introduce novel CFR variants that 1) discount regrets from earlier iterations in various ways (in some cases differently for positive and negative regrets), 2) reweight iterations in various ways to obtain the output strategies, 3) use a non-standard regret minimizer and/or 4) leverage "optimistic regret matching". They lead to dramatically improved performance in many settings. For one, we introduce a variant that outperforms CFR+, the prior state-of-the-art algorithm, in every game tested, including large-scale realistic settings. CFR+ is a formidable benchmark: no other algorithm has been able to outperform it. Finally, we show that, unlike CFR+, many of the important new variants are compatible with modern imperfect-information-game pruning techniques and one is also compatible with sampling in the game tree.

Connected through hyperedge HE-1 (induced by source paper "stochastic regret minimization in extensive form games"):
  with members:
    - "a modern introduction to online learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "regret circuits composability of regret minimizers"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-2 (induced by source paper "combining no regret and q learning"):
  with members:
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "stable predictive optimistic counterfactual regret minimization"
    - "learning to collaborate in markov decision processes"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "deep counterfactual regret minimization"
    - "cycles in adversarial regularized learning"

Connected through hyperedge HE-3 (induced by source paper "single deep counterfactual regret minimization"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "depth limited solving for imperfect information games"
    - "deep counterfactual regret minimization"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization for partially observable deep reinforcement learning"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-4 (induced by source paper "optimistic regret minimization for extensive form games via dilated distance generating functions"):
  with members:
    - "introduction to online convex optimization"
    - "stable predictive optimistic counterfactual regret minimization"
    - "first order methods with increasing iterate averaging for solving saddle point problems"
    - "solving large sequential games with the excessive gap technique"
    - "depth limited solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "imperfect recall abstractions with bounds in games"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-5 (induced by source paper "learning to correlate in multi player general sum sequential games"):
  with members:
    - "computing optimal coarse correlated equilibria in sequential games"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "no regret learning in bayesian games"

Connected through hyperedge HE-6 (induced by source paper "survey of artificial intelligence for card games and its application to the swiss game jass"):
  with members:
    - "improving search with supervised learning in trick based card games"
    - "solving large sequential games with the excessive gap technique"
    - "deep counterfactual regret minimization"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "application of self play reinforcement learning to a four player game of imperfect information"
    - "traditional wisdom and monte carlo tree search face to face in the card game scopone"
    - "evaluating and modelling hanabi playing agents"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "efficient regret minimization algorithm for extensive form correlated equilibrium"):
  with members:
    - "introduction to online convex optimization"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "solving large extensive form games with strategy constraints"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization in behaviorally constrained zero sum games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-8 (induced by source paper "stable predictive optimistic counterfactual regret minimization"):
  with members:
    - "regret circuits composability of regret minimizers"
    - "acceleration through optimistic no regret dynamics"
    - "solving large sequential games with the excessive gap technique"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "depth limited solving for imperfect information games"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "fast convergence of regularized learning in games"

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.GT(Computer Science and Game Theory)