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 "mining software quality from software reviews research trends and open issues"):
  Abstract: Software review text fragments have considerably valuable information about users experience. It includes a huge set of properties including the software quality. Opinion mining or sentiment analysis is concerned with analyzing textual user judgments. The application of sentiment analysis on software reviews can find a quantitative value that represents software quality. Although many software quality methods are proposed they are considered difficult to customize and many of them are limited. This article investigates the application of opinion mining as an approach to extract software quality properties. We found that the major issues of software reviews mining using sentiment analysis are due to software lifecycle and the diverse users and teams.

Includes members:
  Member "building a pilot software quality in use benchmark dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a comprehensive comparative study of word and sentence similarity measures")
  Member "measuring software quality in use state of the art and research challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comprehensive comparative study of word and sentence similarity measures")
  Member "towards resolving software quality in use measurement challenges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "building a pilot software quality in use benchmark dataset")
  Member "feature specific sentiment analysis for product reviews":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sentiment analysis of german twitter")
  Member "modeling online reviews with multi grain topic models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vsec lda boosting topic modeling with embedded vocabulary selection")
  Member "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "measuring praise and criticism inference of semantic orientation from association":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting paraphrase question generator using pairwise discriminator")
  Member "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences")

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.CL(Computation and Language)