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 "is this github project maintained measuring the level of maintenance activity of open source projects"):
  Abstract: Abstract   Context  GitHub hosts an impressive number of high-quality OSS projects. However, selecting “the right tool for the job” is a challenging task, because we do not have precise information about those high-quality projects.    Objective  In this paper, we propose a data-driven approach to measure the level of maintenance activity of GitHub projects. Our goal is to alert users about the risks of using unmaintained projects and possibly motivate other developers to assume the maintenance of such projects.    Method  We train machine learning models to define a metric to express the level of maintenance activity of GitHub projects. Next, we analyze the historical evolution of 2927 active projects in the time frame of one year.    Results  From 2927 active projects, 16% become unmaintained in the interval of one year. We also found that Objective-C projects tend to have lower maintenance activity than projects implemented in other languages. Finally, software tools—such as compilers and editors—have the highest maintenance activity over time.    Conclusions  A metric about the level of maintenance activity of GitHub projects can help developers to select open source projects.

Includes members:
  Member "what s in a github star understanding repository starring practices in a social coding platform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refactoring graphs assessing refactoring over time")
  Member "identifying unmaintained projects in github":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond textual issues understanding the usage and impact of github reactions")
  Member "why we engage in floss answers from core developers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the abandonment and survival of open source projects an empirical investigation")
  Member "why modern open source projects fail":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a topological analysis of communication channels for knowledge sharing in contemporary github projects")
  Member "predicting the popularity of github repositories":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "massive multi agent data driven simulations of the github ecosystem")
  Member "understanding the factors that impact the popularity of github repositories":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refactoring graphs assessing refactoring over time")
  Member "robust classification for imprecise environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges")

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)