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: "operationalised product quality models and assessment"
  Abstract: ContextSoftware quality models provide either abstract quality characteristics or concrete quality measurements; there is no seamless integration of these two aspects. Quality assessment approaches are, hence, also very specific or remain abstract. Reasons for this include the complexity of quality and the various quality profiles in different domains which make it difficult to build operationalised quality models. ObjectiveIn the project Quamoco, we developed a comprehensive approach aimed at closing this gap. MethodThe project combined constructive research, which involved a broad range of quality experts from academia and industry in workshops, sprint work and reviews, with empirical studies. All deliverables within the project were peer-reviewed by two project members from a different area. Most deliverables were developed in two or three iterations and underwent an evaluation. ResultsWe contribute a comprehensive quality modelling and assessment approach: (1) A meta quality model defines the structure of operationalised quality models. It includes the concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects, and allows modularisation to create modules for specific domains. (2) A largely technology-independent base quality model reduces the effort and complexity of building quality models for specific domains. For Java and C# systems, we refined it with about 300 concrete product factors and 500 measures. (3) A concrete and comprehensive quality assessment approach makes use of the concepts in the meta-model. (4) An empirical evaluation of the above results using real-world software systems showed: (a) The assessment results using the base model largely match the expectations of experts for the corresponding systems. (b) The approach and models are well understood by practitioners and considered to be both consistent and well suited for getting an overall view on the quality of a software product. The validity of the base quality model could not be shown conclusively, however. (5) The extensive, open-source tool support is in a mature state. (6) The model for embedded software systems is a proof-of-concept for domain-specific quality models. ConclusionWe provide a broad basis for the development and application of quality models in industrial practice as well as a basis for further extension, validation and comparison with other approaches in research.

Connected through hyperedge HE-1 (induced by source paper "evaluating maintainability prejudices with a large scale study of open source projects"):
  with members:
    - "tool support for continuous quality control"
    - "an activity based quality model for maintainability"

Connected through hyperedge HE-2 (induced by source paper "towards automated data integration in software analytics"):
  with members:
    - "a quality model for actionable analytics in rapid software development"
    - "software analytics to software domains a systematic literature review"

Connected through hyperedge HE-3 (induced by source paper "a quality model for actionable analytics in rapid software development"):
  with members:
    - "explainable software analytics"

Connected through hyperedge HE-4 (induced by source paper "measuring object oriented design principles"):
  with members:
    - "the quamoco product quality modelling and assessment approach"

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.SE(Software Engineering)