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 "algorithms in multi agent systems a holistic perspective from reinforcement learning and game theory"):
  Abstract: Deep reinforcement learning (RL) has achieved outstanding results in recent years, which has led a dramatic increase in the number of methods and applications. Recent works are exploring learning beyond single-agent scenarios and considering multi-agent scenarios. However, they are faced with lots of challenges and are seeking for help from traditional game-theoretic algorithms, which, in turn, show bright application promise combined with modern algorithms and boosting computing power. In this survey, we first introduce basic concepts and algorithms in single agent RL and multi-agent systems; then, we summarize the related algorithms from three aspects. Solution concepts from game theory give inspiration to algorithms which try to evaluate the agents or find better solutions in multi-agent systems. Fictitious self-play becomes popular and has a great impact on the algorithm of multi-agent reinforcement learning. Counterfactual regret minimization is an important tool to solve games with incomplete information, and has shown great strength when combined with deep learning.

Includes members:
  Member "single deep counterfactual regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization")
  Member "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural replicator dynamics")
  Member "bounds for approximate regret matching algorithms":
    also connected through other hyperedges:
      (none)
  Member "combining no regret and q learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "actor critic policy optimization in partially observable multiagent environments")
  Member "a generalized training approach for multiagent learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation")
  Member "stable predictive optimistic counterfactual regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combining no regret and q learning")
  Member "multiagent evaluation under incomplete information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a generalized training approach for multiagent learning")
  Member "a survey and critique of multiagent deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the hanabi challenge a new frontier for ai research")

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)