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 "machine learning based simulation optimisation for trailer management"):
  Abstract: In many situations, simulation models are developed to handle complex real-world business optimisation problems. For example, a discrete-event simulation model is used to simulate the trailer management process in a big Fast-Moving Consumer Goods company. To address the problem of finding suitable inputs to this simulator for optimising fleet configuration, we propose a simulation optimisation approach in this paper. The simulation optimisation model combines a metaheuristic search (genetic algorithm), with an approximation model filter (feed-forward neural network) to optimise the parameter configuration of the simulation model. We introduce an ensure probability that overrules the rejection of potential solutions by the approximation model and we demonstrate its effectiveness. In addition, we evaluate the impact of the parameters of the optimisation model on its effectiveness and show the parameters such as population size, filter threshold, and mutation probability can have a significant impact on the overall optimisation performance. Moreover, we compare the proposed method with a single global approximation model approach and a random-based approach. The results show the effectiveness of our method in terms of computation time and solution quality.

Includes members:
  Member "simulation optimization a review of algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assignment based path choice estimation for metro systems using smart card data")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.NE(Neural and Evolutionary Computing)