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: "multiagent soft q learning"
  Abstract: Policy gradient methods are often applied to reinforcement learning in continuous multiagent games. These methods perform local search in the joint-action space, and as we show, they are susceptable to a game-theoretic pathology known as relative overgeneralization. To resolve this issue, we propose Multiagent Soft Q-learning, which can be seen as the analogue of applying Q-learning to continuous controls. We compare our method to MADDPG, a state-of-the-art approach, and show that our method achieves better coordination in multiagent cooperative tasks, converging to better local optima in the joint action space.

Connected through hyperedge HE-1 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"

Connected through hyperedge HE-2 (induced by source paper "a generalized training approach for multiagent learning"):
  with members:
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "multiagent evaluation under incomplete information"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "openspiel a framework for reinforcement learning in games"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "probabilistic recursive reasoning for multi agent reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the mechanics of n player differentiable games"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "counterfactual multi agent policy gradients"
    - "dual supervised learning"
    - "learning with opponent learning awareness"
    - "the numerics of gans"

Connected through hyperedge HE-4 (induced by source paper "a regularized opponent model with maximum entropy objective"):
  with members:
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning to communicate implicitly by actions"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "machine theory of mind"

Connected through hyperedge HE-5 (induced by source paper "deep coordination graphs"):
  with members:
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "the starcraft multi agent challenge"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "exploration with unreliable intrinsic reward in multi agent reinforcement learning"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-6 (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"
    - "factorized q learning for large scale multi agent systems"
    - "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-7 (induced by source paper "multi agent generalized recursive reasoning"):
  with members:
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "machine theory of mind"
    - "reinforcement learning with deep energy based policies"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "density estimation using real nvp"
    - "opponent modeling in deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep multi agent reinforcement learning with discrete continuous hybrid action spaces"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "playing atari with deep reinforcement learning"

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.AI(Artificial Intelligence)