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 "how to ask for technical help evidence based guidelines for writing questions on stack overflow"):
  Abstract: Abstract   Context  The success of Stack Overflow and other community-based question-and-answer (Q&A) sites depends mainly on the will of their members to answer others’ questions. In fact, when formulating requests on Q&A sites, we are not simply seeking for information. Instead, we are also asking for other people's help and feedback. Understanding the dynamics of the participation in Q&A communities is essential to improve the value of crowdsourced knowledge.    Objective  In this paper, we investigate how information seekers can increase the chance of eliciting a successful answer to their questions on Stack Overflow by focusing on the following actionable factors: affect, presentation quality, and time.    Method  We develop a conceptual framework of factors potentially influencing the success of questions in Stack Overflow. We quantitatively analyze a set of over 87 K questions from the official Stack Overflow dump to assess the impact of actionable factors on the success of technical requests. The information seeker reputation is included as a control factor. Furthermore, to understand the role played by affective states in the success of questions, we qualitatively analyze questions containing positive and negative emotions. Finally, a survey is conducted to understand how Stack Overflow users perceive the guideline suggestions for writing questions.    Results  We found that regardless of user reputation, successful questions are short, contain code snippets, and do not abuse with uppercase characters. As regards affect, successful questions adopt a neutral emotional style.    Conclusion  We provide evidence-based guidelines for writing effective questions on Stack Overflow that software engineers can follow to increase the chance of getting technical help. As for the role of affect, we empirically confirmed community guidelines that suggest avoiding rudeness in question writing.

Includes members:
  Member "semeval 2016 task 4 sentiment analysis in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language")
  Member "bootstrapping a lexicon for emotional arousal in software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time pressure in software engineering a systematic review")
  Member "emotxt a toolkit for emotion recognition from text":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a large scale in depth analysis of developers personalities in the apache ecosystem")
  Member "recommending insightful comments for source code using crowdsourced knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of automatic generation of source code comments algorithms and techniques")
  Member "do feelings matter on the correlation of affects and the self assessed productivity in software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "happiness and the productivity of software engineers")
  Member "how to ask for a favor a case study on the success of altruistic requests":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conversations gone awry detecting early signs of conversational failure")
  Member "the role of emotions in contributors activity a case study on the gentoo community":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sentimoji an emoji powered learning approach for sentiment analysis in software engineering")

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.CY(Computers and Society)