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: "towards resolving software quality in use measurement challenges"
  Abstract: Software quality-in-use comprehends the quality from user’s perspectives. It has gained its importance in e-learning applications, mobile service based applications and project management tools. User’s decisions on software acquisitions are often ad hoc or based on preference due to difficulty in quantitatively measure software quality-in-use. However, why qualityin-use measurement is difficult? Although there are many software quality models to our knowledge, no works surveys the challenges related to software quality-in-use measurement. This paper has two main contributions; 1) presents major issues and challenges in measuring software quality-in-use in the context of the ISO SQuaRE series and related software quality models, 2) Presents a novel framework that can be used to predict software quality-in-use, and 3) presents preliminary results of quality-in-use topic prediction. Concisely, the issues are related to the complexity of the current standard models and the limitations and incompleteness of the customized software quality models. The proposed framework employs sentiment analysis techniques to predict software quality-in-use.

Connected through hyperedge HE-1 (induced by source paper "mining software quality from software reviews research trends and open issues"):
  with members:
    - "building a pilot software quality in use benchmark dataset"
    - "measuring software quality in use state of the art and research 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 "building a pilot software quality in use benchmark dataset"):
  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"

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)