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: "multi agent deep reinforcement learning for large scale traffic signal control"
  Abstract: Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. Multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q-learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent: advantage actor critic (A2C), within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under simulated peak-hour traffic dynamics. Results demonstrate its optimality, robustness, and sample efficiency over other state-of-the-art decentralized MARL algorithms.

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 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"
    - "multi agent imitation learning for driving simulation"

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"
    - "colight learning network level cooperation for 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 "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"
    - "colight learning network level cooperation for 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-4 (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"
    - "colight learning network level cooperation for 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-5 (induced by source paper "colight learning network level cooperation 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"
    - "mean field multi agent reinforcement learning"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "attention is all you need"
    - "deep deterministic policy gradient for urban traffic light control"

Connected through hyperedge HE-6 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "deep reinforcement learning for intelligent transportation systems"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "continuous control with deep reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

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"
    - "colight learning network level cooperation for 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"

Connected through hyperedge HE-8 (induced by source paper "large scale traffic signal control using a novel multi agent reinforcement learning"):
  with members:
    - "mean field multi agent reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "deep deterministic policy gradient for urban traffic light control"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q 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.LG(Machine Learning)