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 "replicating and scaling up qualitative analysis using crowdsourcing a github based case study"):
  Abstract: Due to the difficulties in replicating and scaling up qualitative studies, such studies are rarely verified. Accordingly, in this paper, we leverage the advantages of crowdsourcing (low costs, fast speed, scalable workforce) to replicate and scale-up one state-of-the-art qualitative study. That qualitative study explored 20 GitHub pull requests to learn factors that influence the fate of pull requests with respect to approval and merging. #R##N#As a secondary study, using crowdsourcing at a cost of $200, we studied 250 pull requests from 142 GitHub projects. The prior qualitative findings are mapped into questions for crowds workers. Their answers were converted into binary features to build a predictor which predicts whether code would be merged with median F1 scores of 68%. For the same large group of pull requests, the median F1 scores could achieve 90% by a predictor built with additional features defined by prior quantitative results. #R##N#Based on this case study, we conclude that there is much benefit in combining different kinds of research methods. While qualitative insights are very useful for finding novel insights, they can be hard to scale or replicate. That said, they can guide and define the goals of scalable secondary studies that use (e.g.) crowdsourcing+data mining. On the other hand, while data mining methods are reproducible and scalable to large data sets, their results may be spectacularly wrong since they lack contextual information. That said, they can be used to test the stability and external validity, of the insights gained from a qualitative analysis.

Includes members:
  Member "continuous integration in a social coding world empirical evidence from github":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "replication can improve prior results a github study of pull request acceptance")
  Member "an insight into the pull requests of github":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "which pull requests get accepted and why a study of popular npm packages")

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)