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: "revisiting cfr and alternating updates"
  Abstract: The CFR+ algorithm for solving imperfect information games is a variant of the popular CFR algorithm, with faster empirical performance on a range of problems. It was introduced with a theoretical upper bound on solution error, but subsequent work showed an error in one step of the proof. We provide updated proofs to recover the original bound.

Connected through hyperedge HE-1 (induced by source paper "neural replicator dynamics"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "alpha rank multi agent evaluation by evolution"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "deep counterfactual regret minimization"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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-4 (induced by source paper "deep counterfactual regret minimization"):
  with members:
    - "single deep counterfactual regret minimization"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "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"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "double neural counterfactual regret minimization"

Connected through hyperedge HE-5 (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"
    - "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"
    - "solving large imperfect information games using cfr"
    - "a parameter free hedging algorithm"

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)