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 "efficient regret minimization algorithm for extensive form correlated equilibrium"):
  Abstract: Self-play methods based on regret minimization have become the state of the art for computing Nash equilibria in large two-players zero-sum extensive-form games. These methods fundamentally rely on the hierarchical structure of the players' sequential strategy spaces to construct a regret minimizer that recursively minimizes regret at each decision point in the game tree. In this paper, we introduce the first efficient regret minimization algorithm for computing extensive-form correlated equilibria in large two-player general-sum games with no chance moves. Designing such an algorithm is significantly more challenging than designing one for the Nash equilibrium counterpart, as the constraints that define the space of correlation plans lack the hierarchical structure and might even form cycles. We show that some of the constraints are redundant and can be excluded from consideration, and present an efficient algorithm that generates the space of extensive-form correlation plans incrementally from the remaining constraints. This structural decomposition is achieved via a special convexity-preserving operation that we coin scaled extension. We show that a regret minimizer can be designed for a scaled extension of any two convex sets, and that from the decomposition we then obtain a global regret minimizer. Our algorithm produces feasible iterates. Experiments show that it significantly outperforms prior approaches and for larger problems it is the only viable option.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "correlation in extensive form games saddle point formulation and benchmarks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic regret minimization in extensive form games")
  Member "solving imperfect information games via discounted regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "stochastic regret minimization in extensive form games")
  Member "revisiting cfr and alternating updates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural replicator dynamics")
  Member "solving large extensive form games with strategy constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret circuits composability of regret minimizers")
  Member "safe and nested subgame solving for imperfect information games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "regret circuits composability of regret minimizers")

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.GT(Computer Science and Game Theory)