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: "arsentd lev a multi topic corpus for target based sentiment analysis in arabic levantine tweets"
  Abstract: Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstandardized and widely used in the Web, especially in social media. While many datasets have been released to train sentiment classifiers in Arabic, most of these datasets contain shallow annotation, only marking the sentiment of the text unit, as a word, a sentence or a document. In this paper, we present the Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from analyzing tweets from the Levant region, we created a dataset of 4,000 tweets with the following annotations: the overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. Results confirm the importance of these annotations at improving the performance of a baseline sentiment classifier. They also confirm the gap of training in a certain domain, and testing in another domain.

Connected through hyperedge HE-1 (induced by source paper "arabert transformer based model for arabic language understanding"):
  with members:
    - "towards a human like open domain chatbot"
    - "neural arabic question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "subjective sentiment analysis for arabic newswire comments"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "sentiment analysis for modern standard arabic and colloquial"
    - "an accuracy enhanced stemming algorithm for arabic information retrieval"
    - "a machine learning approach for opinion holder extraction in arabic language"

Connected through hyperedge HE-3 (induced by source paper "aranet a deep learning toolkit for arabic social media"):
  with members:
    - "multi task bidirectional transformer representations for irony detection"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "sentiment analysis for arabic in social media network a systematic mapping study"):
  with members:
    - "sentiment analysis of arabic tweets feature engineering and a hybrid approach"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "a system for extracting sentiment from large scale arabic social data"

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)