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: "strict information retrieval based search term identification for concept location"
  Abstract: During maintenance, software developers deal with numerous change requests that are written in an unstructured fashion using natural language. Such natural language texts illustrate the change requirement involving various domain related concepts. Software developers need to find appropriate search terms from those concepts so that they could locate the possible locations in the source code using a search technique. Once such locations are identified, they can implement the requested changes there. Studies suggest that developers often perform poorly in coming up with good search terms for a change task. In this paper, we propose a novel technique--STRICT--that automatically identifies suitable search terms for a software change task by analyzing its task description using two information retrieval (IR) techniques-- TextRank and POSRank. These IR techniques determine a term's importance based on not only its co-occurrences with other important terms but also its syntactic relationships with them. Experiments using 1,939 change requests from eight subject systems report that STRICT can identify better quality search terms than baseline terms from 52%--62% of the requests with 30%--57% Top-10 retrieval accuracy which are promising. Comparison with two state-of-the-art techniques not only validates our empirical findings and but also demonstrates the superiority of our technique.

Connected through hyperedge HE-1 (induced by source paper "recommending comprehensive solutions for programming tasks by mining crowd knowledge"):
  with members:
    - "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"
    - "comment generation for source code state of the art challenges and opportunities"
    - "rack code search in the ide using crowdsourced knowledge"
    - "more accurate recommendations for method level changes"
    - "nlp2code code snippet content assist via natural language tasks"
    - "enriching word vectors with subword information"
    - "rack automatic api recommendation using crowdsourced knowledge"
    - "deep api learning"

Connected through hyperedge HE-2 (induced by source paper "sieve helping developers sift wheat from chaff via cross platform analysis"):
  with members:
    - "a practitioners guide to transfer learning for text classification using convolutional neural networks"
    - "sentiment polarity detection for software development"
    - "semantically enhanced software traceability using deep learning techniques"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "transfer learning for named entity recognition with neural networks"
    - "enriching word vectors with subword information"
    - "how transferable are neural networks in nlp applications"
    - "an insight into the unresolved questions at stack overflow"

Connected through hyperedge HE-3 (induced by source paper "effective reformulation of query for code search using crowdsourced knowledge and extra large data analytics"):
  with members:
    - "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"
    - "quickar automatic query reformulation for concept location 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-4 (induced by source paper "improved query reformulation for concept location using coderank and document structures"):
  with members:
    - "quickar automatic query reformulation for concept location using crowdsourced knowledge"
    - "textrank based search term identification for software change tasks"
    - "on the use of context in recommending exception handling code examples"

Connected through hyperedge HE-5 (induced by source paper "improving ir based bug localization with context aware query reformulation"):
  with members:
    - "poster improving bug localization with report quality dynamics and query reformulation"
    - "improved query reformulation for concept location using coderank and document structures"
    - "quickar automatic query reformulation for concept location using crowdsourced knowledge"

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)