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: "regret circuits composability of regret minimizers"
  Abstract: Regret minimization is a powerful tool for solving large-scale problems; it was recently used in breakthrough results for large-scale extensive-form game solving. This was achieved by composing simplex regret minimizers into an overall regret-minimization framework for extensive-form game strategy spaces. In this paper we study the general composability of regret minimizers. We derive a calculus for constructing regret minimizers for composite convex sets that are obtained from convexity-preserving operations on simpler convex sets. We show that local regret minimizers for the simpler sets can be combined with additional regret minimizers into an aggregate regret minimizer for the composite set. As one application, we show that the CFR framework can be constructed easily from our framework. We also show ways to include curtailing (constraining) operations into our framework. For one, they enables the construction of CFR generalization for extensive-form games with general convex strategy constraints that can cut across decision points.

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"
    - "solving imperfect information games via discounted regret minimization"
    - "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"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"
    - "cycles in adversarial regularized learning"

Connected through hyperedge HE-3 (induced by source paper "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"):
  with members:
    - "single deep counterfactual regret minimization"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "openspiel a framework for reinforcement learning in games"
    - "neural replicator dynamics"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "deep counterfactual regret minimization"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-4 (induced by source paper "stable predictive optimistic counterfactual regret minimization"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "bounds for approximate regret matching algorithms"):
  with members:
    - "single deep counterfactual regret minimization"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "neural replicator dynamics"
    - "deep counterfactual regret minimization"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "solving games with functional regret estimation"

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.LG(Machine Learning)