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: "colight learning network level cooperation for traffic signal control"
  Abstract: Cooperation among the traffic signals enables vehicles to move through intersections more quickly. Conventional transportation approaches implement cooperation by pre-calculating the offsets between two intersections. Such pre-calculated offsets are not suitable for dynamic traffic environments. To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication. Specifically, for a target intersection in a network, CoLight can not only incorporate the temporal and spatial influences of neighboring intersections to the target intersection, but also build up index-free modeling of neighboring intersections. To the best of our knowledge, we are the first to use graph attentional networks in the setting of reinforcement learning for traffic signal control and to conduct experiments on the large-scale road network with hundreds of traffic signals. In experiments, we demonstrate that by learning the communication, the proposed model can achieve superior performance against the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "gamma reward a novel multi agent reinforcement learning method for traffic signal control"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "a survey on traffic signal control methods"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "ray a distributed framework for emerging ai applications"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "ig rl inductive graph reinforcement learning for massive scale traffic signal control"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "large scale traffic signal control using a novel multi agent reinforcement learning"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "zero shot task generalization with multi task deep reinforcement learning"

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"
    - "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-4 (induced by source paper "stmarl a spatio temporal multi agent reinforcement learning approach for cooperative traffic light control"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "relational deep reinforcement learning"
    - "relational inductive biases deep learning and graph networks"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning phase competition for traffic signal control"):
  with members:
    - "cityflow a multi agent reinforcement learning environment for large scale city traffic scenario"
    - "a survey on traffic signal control methods"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "distributed prioritized experience replay"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "deep deterministic policy gradient for urban traffic light control"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "a survey on traffic signal control methods"):
  with members:
    - "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "learning phase competition for traffic signal control"
    - "deep reinforcement learning a brief survey"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "deep deterministic policy gradient for urban traffic light control"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-7 (induced by source paper "large scale traffic signal control using machine learning some traffic flow considerations"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "continuous control 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.MA(Multiagent Systems)