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: "feudal multi agent hierarchies for cooperative reinforcement learning"
  Abstract: We investigate how reinforcement learning agents can learn to cooperate. Drawing inspiration from human societies, in which successful coordination of many individuals is often facilitated by hierarchical organisation, we introduce Feudal Multi-agent Hierarchies (FMH). In this framework, a 'manager' agent, which is tasked with maximising the environmentally-determined reward function, learns to communicate subgoals to multiple, simultaneously-operating, 'worker' agents. Workers, which are rewarded for achieving managerial subgoals, take concurrent actions in the world. We outline the structure of FMH and demonstrate its potential for decentralised learning and control. We find that, given an adequate set of subgoals from which to choose, FMH performs, and particularly scales, substantially better than cooperative approaches that use a shared reward function.

Connected through hyperedge HE-1 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"
    - "revisiting the master slave architecture in multi agent deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning from hierarchical critics"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "unity a general platform for intelligent agents"
    - "counterfactual multi agent policy gradients"
    - "feudal networks for hierarchical reinforcement learning"
    - "proximal policy optimization algorithms"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "continuous control with deep reinforcement learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "options as responses grounding behavioural hierarchies in multi agent rl"):
  with members:
    - "alpha rank multi agent evaluation by evolution"
    - "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research"
    - "open ended learning in symmetric zero sum games"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "hierarchical visuomotor control of humanoids"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "actor attention critic for multi agent reinforcement learning"
    - "re evaluating evaluation"

Connected through hyperedge HE-4 (induced by source paper "promoting coordination through policy regularization in multi agent deep reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "coride joint order dispatching and fleet management for multi scale ride hailing platforms"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "colight learning network level cooperation for traffic signal control"
    - "data efficient hierarchical reinforcement learning"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "policy search in continuous action domains an overview"
    - "efficient large scale fleet management via multi agent deep reinforcement learning"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-6 (induced by source paper "hierarchical cooperative multi agent reinforcement learning with skill discovery"):
  with members:
    - "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"
    - "on multi agent learning in team sports games"
    - "winning isn t everything training human like agents for playtesting and game ai"
    - "google research football a novel reinforcement learning environment"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "hierarchical critics assignment for multi agent reinforcement learning"):
  with members:
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "data efficient hierarchical reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "unity a general platform for intelligent agents"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical 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.MA(Multiagent Systems)