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: "traffic light control using deep policy gradient and value function based reinforcement learning"
  Abstract: Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces. Inspired by these successes, in this paper, we build two kinds of reinforcement learning algorithms: deep policy-gradient and value-function based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The policy-gradient based agent maps its observation directly to the control signal, however the value-function based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Our methods show promising results in a traffic network simulated in the SUMO traffic simulator, without suffering from instability issues during the training process.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "wildfire monitoring in remote areas using autonomous unmanned aerial vehicles"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "autonomous extraction of a hierarchical structure of tasks in reinforcement learning a sequential associate rule mining approach"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "on greedy routing in dynamic uav networks"
    - "a unified framework for joint mobility prediction and object profiling of drones in uav networks"

Connected through hyperedge HE-3 (induced by source paper "an autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations"):
  with members:
    - "a solution for dynamic spectrum management in mission critical uav networks"
    - "a survey on hardware based security mechanisms for internet of things"
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "waveform and spectrum management for unmanned aerial systems beyond 2025"
    - "spectrum sharing and cyclical multiple access in uav aided cellular offloading"

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"
    - "a deep reinforcement learning network for traffic light cycle control"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "colight learning network level cooperation for traffic signal control"
    - "distributed prioritized experience replay"
    - "deep deterministic policy gradient for urban traffic light control"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "colight learning network level cooperation for traffic signal control"
    - "learning phase competition for traffic signal control"
    - "deep reinforcement learning a brief survey"
    - "deep deterministic policy gradient for urban traffic light control"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-6 (induced by source paper "a deep reinforcement learning network for traffic light cycle control"):
  with members:
    - "deep deterministic policy gradient for urban traffic light control"
    - "adaptive traffic signal control deep reinforcement learning algorithm with experience replay and target network"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "an overview of gradient descent optimization algorithms"
    - "prioritized experience replay"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-7 (induced by source paper "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"):
  with members:
    - "a deep reinforcement learning network for traffic light cycle control"
    - "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"
    - "value decomposition networks for cooperative multi agent learning"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-8 (induced by source paper "a solution for dynamic spectrum management in mission critical uav networks"):
  with members:
    - "ring oscillator and its application as physical unclonable function puf for password management"
    - "overview of blakley s secret sharing scheme"
    - "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"
    - "a reputation based stackelberg game model to enhance secrecy rate in spectrum leasing to selfish iot devices"
    - "leader follower based coalition formation in large scale uav networks a quantum evolutionary approach"
    - "on greedy routing in dynamic uav networks"
    - "deep reinforcement learning an overview"
    - "learning to predict where to look in interactive environments using deep recurrent 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)