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: "factorized q learning for large scale multi agent systems"
  Abstract: Deep Q-learning has achieved a significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics between the environment and the agents. In this paper, we propose to make the computation of multi-agent Q-learning tractable by treating the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional tensor and then approximate it with factorized pairwise interactions. Furthermore, we utilize a composite deep neural network architecture for computing the factorized Q-function, share the model parameters among all the agents within the same group, and estimate the agents' optimal joint actions through a coordinate descent type algorithm. All these simplifications greatly reduce the model complexity and accelerate the learning process. Extensive experiments on two different multi-agent problems have demonstrated the performance gain of our proposed approach in comparison with strong baselines, particularly when there are a large number of agents.

Connected through hyperedge HE-1 (induced by source paper "multi agent game abstraction via graph attention neural network"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "non local neural networks"
    - "attention is all you need"
    - "proximal policy optimization algorithms"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-2 (induced by source paper "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "multiagent soft q learning"
    - "counterfactual multi agent policy gradients"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "from few to more large scale dynamic multiagent curriculum learning"):
  with members:
    - "the starcraft multi agent challenge"
    - "learning transferable cooperative behavior in multi agent teams"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "learning curriculum policies for reinforcement learning"
    - "how powerful are graph neural networks"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-4 (induced by source paper "qmix monotonic value function factorisation for deep multi agent reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "smix lambda enhancing centralized value functions for cooperative multi agent reinforcement learning"
    - "deep multi agent reinforcement learning with discrete continuous hybrid action spaces"
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "hierarchical cooperative multi agent reinforcement learning with skill discovery"
    - "learning nearly decomposable value functions via communication minimization"
    - "the starcraft multi agent challenge"
    - "deep coordination graphs"

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.MA(Multiagent Systems)