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: "deep reinforcement learning an overview"
  Abstract: In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This article reviews the recent advances in deep reinforcement learning with focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.

Connected through hyperedge HE-1 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "autonomous industrial management via reinforcement learning self learning agents for decision making a review"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "a web page classifier library based on random image content analysis using deep learning"
    - "imitation learning with concurrent actions in 3d games"
    - "one shot visual imitation learning via meta learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "deep reinforcement learning a brief survey"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-3 (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-4 (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"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "waveform and spectrum management for unmanned aerial systems beyond 2025"

Connected through hyperedge HE-5 (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"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "learning to predict where to look in interactive environments using deep recurrent q learning"

Connected through hyperedge HE-6 (induced by source paper "the dreaming variational autoencoder for reinforcement learning environments"):
  with members:
    - "learning and querying fast generative models for reinforcement learning"
    - "deep reinforcement learning a brief survey"
    - "hybrid reward architecture for reinforcement learning"
    - "deep reinforcement learning an overview"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "exploration for multi task reinforcement learning with deep generative models"
    - "trust region policy optimization"

Connected through hyperedge HE-7 (induced by source paper "distributed cooperative spectrum sharing in uav networks using multi agent reinforcement learning"):
  with members:
    - "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"
    - "optimal measurement policy for predicting uav network topology"
    - "a coalition formation approach to coordinated task allocation in heterogeneous uav networks"
    - "optimal transport theory for power efficient deployment of unmanned aerial vehicles"
    - "spectrum sharing in cooperative cognitive radio networks a matching game framework"

Connected through hyperedge HE-8 (induced by source paper "traffic light control using deep policy gradient and value function based reinforcement learning"):
  with members:
    - "asynchronous methods for deep reinforcement learning"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "gradient estimation using stochastic computation graphs"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"
    - "experiments with infinite horizon policy gradient estimation"

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)