human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "quickar automatic query reformulation for concept location using crowdsourced knowledge"
  Abstract: During maintenance, software developers deal with numerous change requests made by the users of a software system. Studies show that the developers find it challenging to select appropriate search terms from a change request during concept location. In this paper, we propose a novel technique--QUICKAR--that automatically suggests helpful reformulations for a given query by leveraging the crowdsourced knowledge from Stack Overflow. It determines semantic similarity or relevance between any two terms by analyzing their adjacent word lists from the programming questions of Stack Overflow, and then suggests semantically relevant queries for concept location. Experiments using 510 queries from two software systems suggest that our technique can improve or preserve the quality of 76% of the initial queries on average which is promising. Comparison with one baseline technique validates our preliminary findings, and also demonstrates the potential of our technique.

Connected through hyperedge HE-1 (induced by source paper "boosting api recommendation with implicit feedback"):
  with members:
    - "recommending comprehensive solutions for programming tasks by mining crowd knowledge"
    - "aroma code recommendation via structural code search"
    - "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"
    - "bridging semantic gaps between natural languages and apis with word embedding"
    - "webapirec recommending web apis to software projects via personalized ranking"
    - "xgboost a scalable tree boosting system"
    - "parameter free probabilistic api mining across github"
    - "rack automatic api recommendation using crowdsourced knowledge"

Connected through hyperedge HE-2 (induced by source paper "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "improved query reformulation for concept location using coderank and document structures"
    - "nlp2code code snippet content assist via natural language tasks"
    - "enriching word vectors with subword information"
    - "rack automatic api recommendation using crowdsourced knowledge"
    - "query expansion based on crowd knowledge for code search"
    - "towards a context aware ide based meta search engine for recommendation about programming errors and exceptions"
    - "on the use of context in recommending exception handling code examples"

Connected through hyperedge HE-3 (induced by source paper "improved query reformulation for concept location using coderank and document structures"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "textrank based search term identification for software change tasks"
    - "on the use of context in recommending exception handling code examples"

Connected through hyperedge HE-4 (induced by source paper "improving ir based bug localization with context aware query reformulation"):
  with members:
    - "strict information retrieval based search term identification for concept location"
    - "poster improving bug localization with report quality dynamics and query reformulation"
    - "improved query reformulation for concept location using coderank and document structures"

Question: Please tell me which class the center node 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)