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 "a bayesian network approach to assess and predict software quality using activity based quality models"):
  Abstract: Context: Software quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty. Objective: The qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model. Method: The four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed. Results: The approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions. Conclusion: The approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.

Includes members:
  Member "a bayesian network approach to assess and predict software quality using activity based quality models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "operationalised product quality models and assessment")
  Member "tool support for continuous quality control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating maintainability prejudices with a large scale study of open source projects")
  Member "a comprehensive model of usability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the use of application scanners in software product quality assessment")
  Member "an activity based quality model for maintainability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating maintainability prejudices with a large scale study of open source projects")
  Member "a literature survey of the quality economics of defect detection techniques":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a bayesian network approach to assess and predict software quality using activity based quality models")

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.SE(Software Engineering)