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: "sentiment analysis for modern standard arabic and colloquial"
  Abstract: The rise of social media such as blogs and social networks has fueled interest in sentiment analysis. With the proliferation of reviews, ratings, recommendations and other forms of online expression, online opinion has turned into a kind of virtual currency for businesses looking to market their products, identify new opportunities and manage their reputations, therefore many are now looking to the field of sentiment analysis. In this paper, we present a feature-based sentence level approach for Arabic sentiment analysis. Our approach is using Arabic idioms/saying phrases lexicon as a key importance for improving the detection of the sentiment polarity in Arabic sentences as well as a number of novels and rich set of linguistically motivated features (contextual Intensifiers, contextual Shifter and negation handling), syntactic features for conflicting phrases which enhance the sentiment classification accuracy. Furthermore, we introduce an automatic expandable wide coverage polarity lexicon of Arabic sentiment words. The lexicon is built with gold-standard sentiment words as a seed which is manually collected and annotated and it expands and detects the sentiment orientation automatically of new sentiment words using synset aggregation technique and free online Arabic lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product reviews, etc.). The experimental results using our resources and techniques with SVM classifier indicate high performance levels, with accuracies of over 95%.

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"
    - "an accuracy enhanced stemming algorithm for arabic information retrieval"
    - "a machine learning approach for opinion holder extraction in arabic language"

Connected through hyperedge HE-2 (induced by source paper "a comparative study of feature selection methods for dialectal arabic sentiment classification using support vector machine"):
  with members:
    - "using objective words in the reviews to improve the colloquial arabic sentiment analysis"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "a combined cnn and lstm model for arabic sentiment analysis"):
  with members:
    - "improving sentiment analysis in arabic using word representation"
    - "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection"
    - "combining lexical features and a supervised learning approach for arabic sentiment analysis"
    - "arabic language sentiment analysis on health services"
    - "multichannel variable size convolution for sentence classification"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "enriching word vectors with subword information"
    - "feature representation in convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "using objective words in the reviews to improve the colloquial arabic sentiment analysis"):
  with members:
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "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.CL(Computation and Language)