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: "building a pilot software quality in use benchmark dataset"
  Abstract: Prepared domain specific datasets plays an important role to supervised learning approaches. In this article a new sentence dataset for software quality-in-use is proposed. Three experts were chosen to annotate the data using a proposed annotation scheme. Then the data were reconciled in a (no match eliminate) process to reduce bias. The Kappa, k statistics revealed an acceptable level of agreement; moderate to substantial agreement between the experts. The built data can be used to evaluate software quality-in-use models in sentiment analysis models. Moreover, the annotation scheme can be used to extend the current dataset.

Connected through hyperedge HE-1 (induced by source paper "mining software quality from software reviews research trends and open issues"):
  with members:
    - "measuring software quality in use state of the art and research challenges"
    - "towards resolving software quality in use measurement challenges"
    - "feature specific sentiment analysis for product reviews"
    - "modeling online reviews with multi grain topic models"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "measuring praise and criticism inference of semantic orientation from association"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive comparative study of word and sentence similarity measures"):
  with members:
    - "using latent semantic analysis to identify quality in use qu indicators from user reviews"
    - "measuring software quality in use state of the art and research challenges"
    - "roget s thesaurus and semantic similarity"
    - "text relatedness based on a word thesaurus"
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

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)