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: "a deep reinforcement learning network for traffic light cycle control"
  Abstract: Existing inefficient traffic light cycle control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. Existing works either split the traffic signal into equal duration or only leverage limited traffic information. In this paper, we study how to decide the traffic signal duration based on the collected data from different sensors. We propose a deep reinforcement learning model to control the traffic light cycle. In the model, we quantify the complex traffic scenario as states by collecting traffic data and dividing the whole intersection into small grids. The duration changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards. The proposed model incorporates multiple optimization elements to improve the performance, such as dueling network, target network, double Q-learning network, and prioritized experience replay. We evaluate our model via simulation on a Simulation of Urban MObility simulator. Simulation results show the efficiency of our model in controlling traffic lights.

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"
    - "colight learning network level cooperation for traffic signal 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"
    - "stmarl a spatio temporal multi agent reinforcement learning approach for traffic light control"
    - "multi agent deep reinforcement learning for large scale traffic signal 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 "efficiency and equity are both essential a generalized traffic signal controller with deep reinforcement learning"):
  with members:
    - "a survey on traffic signal control methods"
    - "proximal policy optimization algorithms"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-4 (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"
    - "multi agent deep reinforcement learning for large scale traffic signal 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-5 (induced by source paper "tensor based cooperative control for large scale multi intersection traffic signal using deep reinforcement learning and imitation learning"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "deep deterministic policy gradient for urban traffic light control"
    - "constrained policy optimization"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "trust region policy optimization"

Connected through hyperedge HE-6 (induced by source paper "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "cm3 cooperative multi goal multi stage multi agent reinforcement learning"
    - "flow a modular learning framework for autonomy in traffic"
    - "counterfactual multi agent policy gradients"
    - "intelligent traffic light control using distributed multi agent q learning"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "value decomposition networks for cooperative multi agent learning"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-7 (induced by source paper "intelligent coordination among multiple traffic intersections using multi agent reinforcement learning"):
  with members:
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep reinforcement learning that matters"
    - "deep reinforcement learning a brief survey"
    - "adaptive traffic signal control deep reinforcement learning algorithm with experience replay and target network"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (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"
    - "multi agent deep reinforcement learning for large scale traffic signal 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"

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)