human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "solving imperfect information games via discounted regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic regret minimization in extensive form games")
  Member "online convex optimization for sequential decision processes and extensive form games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic regret minimization in extensive form games")
  Member "solving large extensive form games with strategy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient regret minimization algorithm for extensive form correlated equilibrium")
  Member "what game are we playing end to end learning in normal and extensive form games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable convex optimization layers")
  Member "smoothing method for approximate extensive form perfect equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for green security games with real time information")
  Member "safe and nested subgame solving for imperfect information games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coordination in adversarial sequential team games via multi agent deep reinforcement learning")
  Member "regret minimization in behaviorally constrained zero sum games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient regret minimization algorithm for extensive form correlated equilibrium")
  Member "deepstack expert level artificial intelligence in heads up no limit poker":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")

Question: Please tell me which class the source paper 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)