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: "nrc canada building the state of the art in sentiment analysis of tweets"
  Abstract: In this paper, we describe how we created two state-of-the-art SVM classifiers, one to detect the sentiment of messages such as tweets and SMS (message-level task) and one to detect the sentiment of a term within a submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated us available resources.

Connected through hyperedge HE-1 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-2 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis is not solved assessing and probing sentiment classification"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reducing gender bias in abusive language detection"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-4 (induced by source paper "targeted sentiment classification with attentional encoder network"):
  with members:
    - "transformation networks for target oriented sentiment classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "interactive attention networks for aspect level sentiment classification"
    - "aspect level sentiment classification with deep memory network"
    - "wavenet a generative model for raw audio"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-5 (induced by source paper "an efficient model for sentiment analysis of electronic product reviews in vietnamese"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "squeeze and excitation networks"
    - "convolutional sequence to sequence learning"
    - "long short term memory networks for machine reading"
    - "enriching word vectors with subword information"
    - "hierarchical question image co attention for visual question answering"

Connected through hyperedge HE-6 (induced by source paper "word embeddings for sentiment analysis a comprehensive empirical survey"):
  with members:
    - "a large self annotated corpus for sarcasm"
    - "quality of word embeddings on sentiment analysis tasks"
    - "image based recommendations on styles and substitutes"
    - "retrofitting word vectors to semantic lexicons"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "a convolutional neural network for modelling sentences"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers"):
  with members:
    - "automatic stance detection using end to end memory networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm"
    - "stance detection with bidirectional conditional encoding"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-8 (induced by source paper "twitter sentiment on affordable care act using score embedding"):
  with members:
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "distributed representations of sentences and documents"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)