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: "arabic language sentiment analysis on health services"
  Abstract: The social media network phenomenon creates massive amounts of valuable data that is available online and easy to access. Many users share images, videos, comments, reviews, news and opinions on different social networks sites, with Twitter being one of the most popular ones. Data collected from Twitter is highly unstructured, and extracting useful information from tweets is a challenging task. Twitter has a huge number of Arabic users who mostly post and write their tweets using the Arabic language. While there has been a lot of research on sentiment analysis in English, the amount of researches and datasets in Arabic language is limited. This paper introduces an Arabic language dataset, which is about opinions on health services and has been collected from Twitter. The paper will first detail the process of collecting the data from Twitter and also the process of filtering, pre-processing and annotating the Arabic text in order to build a big sentiment analysis dataset in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector Machine and Logistic Regression) alongside Deep and Convolutional Neural Networks were utilized in our experiments of sentiment analysis on our health dataset.

Connected through hyperedge HE-1 (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"
    - "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"
    - "sentiment analysis for modern standard arabic and colloquial"

Connected through hyperedge HE-2 (induced by source paper "improving sentiment analysis in arabic using word representation"):
  with members:
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "1 5 billion words arabic corpus"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "sentialg automated corpus annotation for algerian sentiment analysis"):
  with members:
    - "asda analyseur syntaxique du dialecte alg e rien dans un but d analyse s e mantique"
    - "distributed representations of sentences and documents"
    - "crowdsourcing a word emotion association lexicon"

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)