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: "a machine learning approach for opinion holder extraction in arabic language"
  Abstract: Opinion mining aims at extracting useful subjective information from reliable amounts of text. Opinion mining holder recognition is a task that has not been considered yet in Arabic Language. This task essentially requires deep understanding of clauses structures. Unfortunately, the lack of a robust, publicly available, Arabic parser further complicates the research. This paper presents a leading research for the opinion holder extraction in Arabic news independent from any lexical parsers. We investigate constructing a comprehensive feature set to compensate the lack of parsing structural outcomes. The proposed feature set is tuned from English previous works coupled with our proposed semantic field and named entities features. Our feature analysis is based on Conditional Random Fields (CRF) and semi-supervised pattern recognition techniques. Different research models are evaluated via cross-validation experiments achieving 54.03 F-measure. We publicly release our own research outcome corpus and lexicon for opinion mining community to encourage further research.

Connected through hyperedge HE-1 (induced by source paper "subjective sentiment analysis for arabic newswire comments"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "arsentd lev a multi topic corpus for target based sentiment analysis in arabic levantine tweets"
    - "sentiment analysis for modern standard arabic and colloquial"
    - "an accuracy enhanced stemming algorithm for arabic information retrieval"

Connected through hyperedge HE-2 (induced by source paper "semeval 2017 task 4 sentiment analysis in twitter"):
  with members:
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "amobee at semeval 2017 task 4 deep learning system for sentiment detection on twitter"
    - "putting things in context community specific embedding projections for sentiment analysis"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis for arabic language a brief survey of approaches and techniques"):
  with members:
    - "a system for extracting sentiment from large scale arabic social data"
    - "idioms proverbs lexicon for modern standard arabic and colloquial sentiment analysis"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-4 (induced by source paper "smarties sentiment models for arabic target entities"):
  with members:
    - "effective lstms for target dependent sentiment classification"
    - "entity specific sentiment classification of yahoo news comments"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "sentiment subjectivity analysis survey for languages other than english"):
  with members:
    - "language independent sentiment analysis using subjectivity and positional information"
    - "twitter mood predicts the stock market"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-6 (induced by source paper "sentiment analysis for modern standard arabic and colloquial"):
  with members:
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "thumbs up sentiment classification using machine learning techniques"

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.IR(Information Retrieval)