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: "adapting software quality models practical challenges approach and first empirical results"
  Abstract: Measuring and evaluating software quality has become a fundamental task. Many models have been proposed to support stakeholders in dealing with software quality. However, in most cases, quality models do not fit perfectly for the target application context. Since approaches for efficiently adapting quality models are largely missing, many quality models in practice are built from scratch or reuse only high-level concepts of existing models. We present a tool-supported approach for the efficient adaptation of quality models. An initial empirical investigation indicates that the quality models obtained applying the proposed approach are considerably more consistently and appropriately adapted than those obtained following an ad-hoc approach. Further, we could observe that model adaptation is significantly more efficient (~factor 8) when using this approach.

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"
    - "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"
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "tool support for continuous quality control"
    - "an activity based quality model for maintainability"

Connected through hyperedge HE-2 (induced by source paper "the quamoco product quality modelling and assessment approach"):
  with members:
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "do code clones matter"
    - "a bayesian network approach to assess and predict software quality using activity based quality models"
    - "an activity based quality model for maintainability"

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)