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: "semeval 2014 task 9 sentiment analysis in twitter"
  Abstract: We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1-score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.

Connected through hyperedge HE-1 (induced by source paper "a transformer based approach to irony and sarcasm detection"):
  with members:
    - "spanbert improving pre training by representing and predicting spans"
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "a survey on sentiment and emotion analysis for computational literary studies"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "semeval 2015 task 10 sentiment analysis in twitter"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "talaia a real time monitor of social media and digital press"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "semi supervised sequence learning"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "distributed representations of words and phrases and their compositionality"
    - "twitter mood predicts the stock market"

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)