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 "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"):
  Abstract: Software developers frequently issue generic natural language queries for code search while using code search engines (e.g., GitHub native search, Krugle). Such queries often do not lead to any relevant results due to vocabulary mismatch problems. In this paper, we propose a novel technique that automatically identifies relevant and specific API classes from Stack Overflow Q & A site for a programming task written as a natural language query, and then reformulates the query for improved code search. We first collect candidate API classes from Stack Overflow using pseudo-relevance feedback and two term weighting algorithms, and then rank the candidates using Borda count and semantic proximity between query keywords and the API classes. The semantic proximity has been determined by an analysis of 1.3 million questions and answers of Stack Overflow. Experiments using 310 code search queries report that our technique suggests relevant API classes with 48% precision and 58% recall which are 32% and 48% higher respectively than those of the state-of-the-art. Comparisons with two state-of-the-art studies and three popular search engines (e.g., Google, Stack Overflow, and GitHub native search) report that our reformulated queries (1) outperform the queries of the state-of-the-art, and (2) significantly improve the code search results provided by these contemporary search engines.

Includes members:
  Member "strict information retrieval based search term identification for concept location":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge")
  Member "improved query reformulation for concept location using coderank and document structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving ir based bug localization with context aware query reformulation")
  Member "nlp2code code snippet content assist via natural language tasks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge")
  Member "enriching word vectors with subword information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "rack automatic api recommendation using crowdsourced knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "boosting api recommendation with implicit feedback")
  Member "quickar automatic query reformulation for concept location using crowdsourced knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "boosting api recommendation with implicit feedback")
  Member "query expansion based on crowd knowledge for code search":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "boosting api recommendation with implicit feedback")
  Member "towards a context aware ide based meta search engine for recommendation about programming errors and exceptions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "strict information retrieval based search term identification for concept location")

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)