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: "bilingual sentiment embeddings joint projection of sentiment across languages"
  Abstract: Sentiment analysis in low-resource languages suffers from a lack of annotated corpora to estimate high-performing models. Machine translation and bilingual word embeddings provide some relief through cross-lingual sentiment approaches. However, they either require large amounts of parallel data or do not sufficiently capture sentiment information. We introduce Bilingual Sentiment Embeddings (BLSE), which jointly represent sentiment information in a source and target language. This model only requires a small bilingual lexicon, a source-language corpus annotated for sentiment, and monolingual word embeddings for each language. We perform experiments on three language combinations (Spanish, Catalan, Basque) for sentence-level cross-lingual sentiment classification and find that our model significantly outperforms state-of-the-art methods on four out of six experimental setups, as well as capturing complementary information to machine translation. Our analysis of the resulting embedding space provides evidence that it represents sentiment information in the resource-poor target language without any annotated data in that language.

Connected through hyperedge HE-1 (induced by source paper "irony detection in a multilingual context"):
  with members:
    - "a corpus of english hindi code mixed tweets for sarcasm detection"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "learning word vectors for 157 languages"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "unsupervised neural machine translation"
    - "word translation without parallel data"
    - "improving multilingual named entity recognition with wikipedia entity type mapping"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (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"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "learning cross lingual word embeddings from twitter via distant supervision"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "meemi finding the middle ground in cross lingual word embeddings"
    - "unsupervised multilingual word embeddings"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-4 (induced by source paper "unisent universal adaptable sentiment lexica for 1000 languages"):
  with members:
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "leveraging large amounts of weakly supervised data for multi language sentiment classification"
    - "word translation without parallel data"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "enriching word vectors with subword information"
    - "comparing fifty natural languages and twelve genetic languages using word embedding language divergence weld as a quantitative measure of language distance"
    - "ultradense word embeddings by orthogonal transformation"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-5 (induced by source paper "on the effect of word order on cross lingual sentiment analysis"):
  with members:
    - "unsupervised statistical machine translation"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "universal language model fine tuning for text classification"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "neural structural correspondence learning for domain adaptation"
    - "unsupervised cross domain word representation learning"
    - "exploiting similarities among languages for machine translation"
    - "marginalized denoising autoencoders for domain adaptation"
    - "frustratingly easy domain adaptation"

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)