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: "a bayesian network approach to assess and predict software quality using activity based quality models"
  Abstract: Assessing and predicting the complex concept of software quality is still challenging in practice as well as research. Activity-based quality models break down this complex concept into more concrete definitions, more precisely facts about the system, process and environment and their impact on activities performed on and with the system. However, these models lack an operationalisation that allows to use them in assessment and prediction of quality. Bayesian Networks (BN) have been shown to be a viable means for assessment and prediction incorporating variables with uncertainty. This paper describes how activity-based quality models can be used to derive BN models for quality assessment and prediction. The proposed approach is demonstrated in a proof of concept using publicly available data.

Connected through hyperedge HE-1 (induced by source paper "operationalised product quality models and assessment"):
  with members:
    - "introduction of static quality analysis in small and medium sized software enterprises experiences from technology transfer"
    - "the quamoco product quality modelling and assessment approach"
    - "adapting software quality models practical challenges approach and first empirical results"
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "model based product quality evaluation with multi criteria decision analysis"
    - "do code clones matter"
    - "tool support for continuous quality control"
    - "an activity based quality model for maintainability"

Connected through hyperedge HE-2 (induced by source paper "are comprehensive quality models necessary for evaluating software quality"):
  with members:
    - "the quamoco product quality modelling and assessment approach"
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "tool support for continuous quality control"

Connected through hyperedge HE-3 (induced by source paper "the quamoco product quality modelling and assessment approach"):
  with members:
    - "adapting software quality models practical challenges approach and first empirical results"
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "do code clones matter"
    - "an activity based quality model for maintainability"

Connected through hyperedge HE-4 (induced by source paper "the use of application scanners in software product quality assessment"):
  with members:
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "tool support for continuous quality control"
    - "a comprehensive model of usability"
    - "an activity based quality model for maintainability"

Connected through hyperedge HE-5 (induced by source paper "a bayesian network approach to assess and predict software quality using activity based quality models"):
  with members:
    - "tool support for continuous quality control"
    - "a comprehensive model of usability"
    - "an activity based quality model for maintainability"
    - "a literature survey of the quality economics of defect detection techniques"

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)