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 "estimating the accuracy of the return on investment roi performance evaluations"):
  Abstract: Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The ROI sensitivity to error is a natural thought, and common sense suggests that ROI evaluations cannot be absolutely accurate. However, literature review revealed that in most publications and analyst firms reports, this issue is just overlooked. On the one hand, the results of the ROI calculations are implied to be produced with a mathematical rigor, possibility of errors is not mentioned and amount of errors is not estimated. On the contrary, another approach claims ROI evaluations to be absolutely inaccurate because, in view of their authors, future benefits (especially, intangible) cannot be estimated within any reasonable boundaries. The purpose of this study is to provide a systematic research of the accuracy of the ROI evaluations in the context of the information systems implementations. The main contribution of the study is that this is the first systematic effort to evaluate ROI accuracy. Analytical expressions have been derived for estimating errors of the ROI evaluations. Results of the Monte Carlo simulation will help practitioners in making informed decisions based on explicitly stated factors influencing the ROI uncertainties. The results of this research are intended for researchers in information systems, technology solutions and business management, and also for information specialists, project managers, program managers, technology directors, and information systems evaluators. Most results are applicable to ROI evaluations in a wider subject area.

Includes members:
  Member "assessing excel vba suitability for monte carlo simulation":
    also connected through other hyperedges:
      (none)
  Member "a hybrid intelligent model for software cost estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "software development effort estimation using regression fuzzy models")
  Member "analysis of empirical software effort estimation models":
    also connected through other hyperedges:
      (none)
  Member "software effort estimation using radial basis and generalized regression neural networks":
    also connected through other hyperedges:
      (none)

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)