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 "multi agent game abstraction via graph attention neural network"):
  Abstract: In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.

Includes members:
  Member "qmix monotonic value function factorisation for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "learning attentional communication for multi agent cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "factorized q learning for large scale multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning")
  Member "learning when to communicate at scale in multiagent cooperative and competitive tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "graph convolutional reinforcement learning for multi agent cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph")
  Member "non local neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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.AI(Artificial Intelligence)