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 "the effects of gqm strategies on organizational alignment"):
  Abstract: The increasing role of software for developing products and services requires that organizations align their software-related activities with high-level business goals. In practice, this alignment is very difficult and only little systematic support is available. GQM + Strategies is a method that aims at aligning organizational goals, strategies, and measurements at all levels of an organization in a seamless way. This article describes a case study of applying GQM + Strategies in a globally operating industrial R&D organization developing special-purpose device products for B2B customers. The study analyzes how GQM + Strategies has helped clarify and harmonize the goal set of the organization. Results of the study indicate improved alignment and integration of different goals. In addition, the method helped to make the initially informal goal-setting more transparent and consequently enabled revising it while new, more important goals were discovered and comprehended. Moreover, several elements affecting the achievement of goals as well as impediments were identified.

Includes members:
  Member "gqm strategies a comprehensive methodology for aligning business strategies with software measurement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a measurement based software quality framework")
  Member "experiences and insights from applying gqm strategies in a systems product development organisation":
    also connected through other hyperedges:
      (none)
  Member "linking software development and business strategy through measurement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards identifying and managing sources of uncertainty in ai and machine learning models an overview")

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)