human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "smart train operation algorithms based on expert knowledge and reinforcement learning"):
  Abstract: During recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems. On the one hand, it is more intelligent than traditional manual driving; on the other hand, it increases the energy consumption and decreases the riding comfort of the subway system. This paper proposes two smart train operation algorithms based on the combination of expert knowledge and reinforcement learning algorithms. Compared with previous works, smart train operation algorithms can realize the control of continuous action for the subway system and satisfy multiple objectives (the safety, the punctuality, the energy efficiency, and the riding comfort) without using an offline optimized speed profile. Firstly, through analyzing historical data of experienced subway drivers, we summarize the expert knowledge rules and build inference methods to guarantee the riding comfort, the punctuality and the safety of the subway system. Then we develop two algorithms to realize the control of continuous action and to ensure the energy efficiency of train operation. Among them, one is the smart train operation (STO) algorithm based on deep deterministic policy gradient named (STOD) and another is the smart train operation algorithm based on normalized advantage function (STON). Finally, we verify the performance of proposed algorithms via some numerical simulations with the real field data collected from the Yizhuang Line of the Beijing Subway and their performance will be compared with existing ATO algorithms. The results of numerical simulations show that the developed smart train operation systems are better than manual driving and existing ATO algorithms in respect of energy efficiency. In addition, STOD and STON have the ability to adapt to different trip times and different resistance conditions.

Includes members:
  Member "learning agent for a heat pump thermostat with a set back strategy using model free reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of reinforcement learning for autonomous building energy management")
  Member "continuous control with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "playing atari with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

Question: Please tell me which class the source paper 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.CE(Computational Engineering, Finance, and Science)