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 usage of web search for software engineering"):
  Abstract: Internet plays a key role in accomplishing many tasks. For many such tasks, web search is integral in finding relevant information. Similar to other domains, web search is also heavily used in software engineering (SE) to help with various SE specific tasks such as debugging, finding documentation, installation, etc. In this paper, we present the first large scale study on how web search is used in software engineering. We analyze the query logs from a major commercial web search engine. Being able to disambiguate software engineering queries from other queries is important for understanding the usage of web search in software engineering. We build a machine learning based classifier for distinguishing software engineering related search queries from other queries. We then define the taxonomy of intents behind the usage of web search by software engineers. This allows us to develop a better understanding of the various contexts in which web search is used in software engineering. We also analyze 1 million web search sessions to understand how software engineering related queries and sessions differ from other queries and sessions. Our results show that web search is heavily used for SE related search tasks. Finally, we discuss implications of this work on improving search engine support for developers and providing more effective and contextual assistance to developers using web resources. To our knowledge, this is the first study to fully characterize online search tasks in the software engineering context with a focus on query and session level differences.

Includes members:
  Member "interactions between health searchers and search engines":
    also connected through other hyperedges:
      (none)
  Member "auditing search engines for differential satisfaction across demographics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a mulching proposal")
  Member "query chains learning to rank from implicit feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semeval 2016 task 3 community question answering")

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)