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 large imperfect information games using cfr"
  Abstract: Counterfactual Regret Minimization and variants (e.g. Public Chance Sampling CFR and Pure CFR) have been known as the best approaches for creating approximate Nash equilibrium solutions for imperfect information games such as poker. This paper introduces CFR$^+$, a new algorithm that typically outperforms the previously known algorithms by an order of magnitude or more in terms of computation time while also potentially requiring less memory.

Connected through hyperedge HE-1 (induced by source paper "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory"):
  with members:
    - "single deep counterfactual regret minimization"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "bounds for approximate regret matching algorithms"
    - "combining no regret and q learning"
    - "a generalized training approach for multiagent learning"
    - "stable predictive optimistic counterfactual regret minimization"
    - "multiagent evaluation under incomplete information"
    - "a survey and critique of multiagent deep reinforcement learning"

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"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"

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"
    - "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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "neural fictitious self play on elf mini rts"):
  with members:
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "learning with opponent learning awareness"
    - "starcraft ii a new challenge for reinforcement learning"
    - "hybrid reward architecture for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning from self play in imperfect information games"
    - "torchcraft a library for machine learning research on real time strategy games"

Connected through hyperedge HE-5 (induced by source paper "improving fictitious play reinforcement learning with expanding models"):
  with members:
    - "neural fictitious self play on elf mini rts"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "openspiel a framework for reinforcement learning in games"
    - "open ended learning in symmetric zero sum games"
    - "deep counterfactual regret minimization"
    - "balancing two player stochastic games with soft q learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "a unified game theoretic approach to multiagent reinforcement learning"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "solving imperfect information games via discounted regret minimization"):
  with members:
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "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"
    - "a parameter free hedging algorithm"

Connected through hyperedge HE-8 (induced by source paper "analysis and optimization of deep counterfactual value networks"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "adam a method for stochastic optimization"
    - "solving imperfect information games using decomposition"
    - "measuring the size of large no limit poker 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)