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: "safe and nested subgame solving for imperfect information games"
  Abstract: In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames. Thus a subgame cannot be solved in isolation and must instead consider the strategy for the entire game as a whole, unlike perfect-information games. Nevertheless, it is possible to first approximate a solution for the whole game and then improve it by solving individual subgames. This is referred to as subgame solving. We introduce subgame-solving techniques that outperform prior methods both in theory and practice. We also show how to adapt them, and past subgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the prior state-of-the-art approach, action translation. Finally, we show that subgame solving can be repeated as the game progresses down the game tree, leading to far lower exploitability. These techniques were a key component of Libratus, the first AI to defeat top humans in heads-up no-limit Texas hold'em poker.

Connected through hyperedge HE-1 (induced by source paper "coordination in adversarial sequential team games via multi agent deep reinforcement learning"):
  with members:
    - "signal instructed coordination in cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "signal instructed coordination in team competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "deep counterfactual regret minimization"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-2 (induced by source paper "single deep counterfactual regret minimization"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "solving imperfect information games via discounted regret minimization"
    - "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"
    - "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-3 (induced by source paper "low variance and zero variance baselines for extensive form games"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "depth limited solving for imperfect information games"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "aivat a new variance reduction technique for agent evaluation in imperfect information games"

Connected through hyperedge HE-4 (induced by source paper "learning to correlate in multi player general sum sequential games"):
  with members:
    - "computing optimal coarse correlated equilibria in sequential games"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"
    - "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-5 (induced by source paper "rlcard a toolkit for reinforcement learning in card games"):
  with members:
    - "neural mmo a massively multiagent game environment for training and evaluating intelligent agents"
    - "openspiel a framework for reinforcement learning in games"
    - "deep counterfactual regret minimization"
    - "unity a general platform for intelligent agents"
    - "virtual taobao virtualizing real world online retail environment for reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep reinforcement learning an overview"
    - "a unified game theoretic approach to multiagent reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "solving large extensive form games with strategy constraints"
    - "regret minimization in behaviorally constrained zero sum games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-7 (induced by source paper "learning probably approximately correct maximin strategies in simulation based games with infinite strategy spaces"):
  with members:
    - "competitive bridge bidding with deep neural networks"
    - "multiagent evaluation under incomplete information"
    - "large scale learning of agent rationality in two player zero sum games"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "a generalised method for empirical game theoretic analysis"
    - "bandit learning in concave n person games"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "on kernelized multi armed bandits"

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 imperfect information games via discounted regret minimization"
    - "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"
    - "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.AI(Artificial Intelligence)