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: "learning word vectors for 157 languages"
  Abstract: Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "lost in embedding space explaining cross lingual task performance with eigenvalue divergence"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "what do language representations really represent"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "investigating multilingual nmt representations at scale"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "do we really need fully unsupervised cross lingual embeddings"

Connected through hyperedge HE-3 (induced by source paper "word sense disambiguation for 158 languages using word embeddings only"):
  with members:
    - "glossbert bert for word sense disambiguation with gloss knowledge"
    - "multi sense embeddings from topic models"
    - "watset local global graph clustering with applications in sense and frame induction"
    - "an unsupervised word sense disambiguation system for under resourced languages"
    - "joint multilingual supervision for cross lingual entity linking"
    - "russian word sense induction by clustering averaged word embeddings"
    - "autosense model for word sense induction"
    - "word sense induction with neural bilm and symmetric patterns"

Connected through hyperedge HE-4 (induced by source paper "unsupervised sentiment analysis for code mixed data"):
  with members:
    - "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
    - "a survey of cross lingual word embedding models"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "sentiment analysis of code mixed languages leveraging resource rich languages"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "irony detection in a multilingual context"):
  with members:
    - "a corpus of english hindi code mixed tweets for sarcasm detection"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "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-6 (induced by source paper "comparison between traditional machine learning models and neural network models for vietnamese hate speech detection"):
  with members:
    - "emotion recognition for vietnamese social media text"
    - "job prediction from deep neural network models to applications"
    - "vais hate speech detection system a deep learning based approach for system combination"
    - "hate speech detection on vietnamese social media text using the bidirectional lstm model"
    - "convolutional neural networks for toxic comment classification"
    - "detecting hate speech and offensive language on twitter using machine learning an n gram and tfidf based approach"
    - "hate speech detection a solved problem the challenging case of long tail on twitter"
    - "automated hate speech detection and the problem of offensive language"

Connected through hyperedge HE-7 (induced by source paper "combining pretrained high resource embeddings and subword representations for low resource languages"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "opennmt open source toolkit for neural machine translation"
    - "enriching word vectors with subword information"
    - "learning to understand phrases by embedding the dictionary"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "comparing rule based feature based and deep neural methods for de identification of dutch medical records"):
  with members:
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer learning for named entity recognition with neural networks"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "de identification of patient notes with recurrent neural networks"
    - "more accurate tests for the statistical significance of result differences"

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)