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 deterministic policy gradient for urban traffic light control"
  Abstract: Traffic light timing optimization is still an active line of research despite the wealth of scientific literature on the topic, and the problem remains unsolved for any non-toy scenario. One of the key issues with traffic light optimization is the large scale of the input information that is available for the controlling agent, namely all the traffic data that is continually sampled by the traffic detectors that cover the urban network. This issue has in the past forced researchers to focus on agents that work on localized parts of the traffic network, typically on individual intersections, and to coordinate every individual agent in a multi-agent setup. In order to overcome the large scale of the available state information, we propose to rely on the ability of deep Learning approaches to handle large input spaces, in the form of Deep Deterministic Policy Gradient (DDPG) algorithm. We performed several experiments with a range of models, from the very simple one (one intersection) to the more complex one (a big city section).

Connected through hyperedge HE-1 (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"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "using a deep reinforcement learning agent for traffic signal control"

Connected through hyperedge HE-3 (induced by source paper "a deep reinforcement learning network for traffic light cycle control"):
  with members:
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "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-4 (induced by source paper "tensor based cooperative control for large scale multi intersection traffic signal using deep reinforcement learning and imitation learning"):
  with members:
    - "a deep reinforcement learning network for traffic light cycle control"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "efficient entropy for policy gradient with multidimensional action space"
    - "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-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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "diagnosing reinforcement learning for traffic signal control"):
  with members:
    - "a deep reinforcement learning network for traffic light cycle control"
    - "interpretable policies for reinforcement learning by genetic programming"
    - "traffic light control using deep policy gradient and value function based reinforcement learning"
    - "adaptive traffic signal control deep reinforcement learning algorithm with experience replay and target network"
    - "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 a novel multi agent reinforcement learning"):
  with members:
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "mean field multi agent reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "multi agent deep reinforcement learning for large scale traffic signal control"):
  with members:
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "using a deep reinforcement learning agent for traffic signal control"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

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.NE(Neural and Evolutionary Computing)