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: "efficient large scale fleet management via multi agent deep reinforcement learning"
  Abstract: Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.

Connected through hyperedge HE-1 (induced by source paper "reward design for driver repositioning using multi agent reinforcement learning"):
  with members:
    - "multi agent reinforcement learning for order dispatching via order vehicle distribution matching"
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "optimal passenger seeking policies on e hailing platforms using markov decision process and imitation learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a probabilistic simulator of spatial demand for product allocation"):
  with members:
    - "model based reinforcement learning for atari"
    - "playing atari with deep reinforcement learning"
    - "a data mining framework for optimal product selection in retail supermarket data the generalized profset model"

Connected through hyperedge HE-3 (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"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "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"
    - "learning multi level hierarchies with hindsight"
    - "meta learning shared hierarchies"

Connected through hyperedge HE-4 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies"):
  with members:
    - "leveraging statistical multi agent online planning with emergent value function approximation"
    - "open loop execution of tree search algorithms"
    - "machine theory of mind"
    - "feedback based tree search for reinforcement learning"
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a structured prediction approach for generalization in cooperative multi agent reinforcement learning"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "relational deep reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "counterfactual multi agent policy gradients"
    - "non local neural networks"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"

Connected through hyperedge HE-6 (induced by source paper "orl reinforcement learning benchmarks for online stochastic optimization problems"):
  with members:
    - "learning dexterous in hand manipulation"
    - "distributed prioritized experience replay"
    - "rllib abstractions for distributed reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "a deep q network for the beer game a deep reinforcement learning algorithm to solve inventory optimization problems"
    - "proximal policy optimization algorithms"
    - "neural combinatorial optimization with reinforcement learning"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-7 (induced by source paper "multi agent reinforcement learning for order dispatching via order vehicle distribution matching"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "learning to communicate to solve riddles with deep distributed recurrent q networks"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "multi agent hierarchical reinforcement learning with dynamic termination"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "learning with options that terminate off policy"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "the option critic architecture"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

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)