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: "multilingual models for compositional distributed semantics"
  Abstract: We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages. We extend our approach to learn semantic representations at the document level, too. We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art. Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.

Connected through hyperedge HE-1 (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"
    - "learning word vectors for 157 languages"
    - "sentiment analysis of code mixed languages leveraging resource rich languages"
    - "word translation without parallel data"

Connected through hyperedge HE-2 (induced by source paper "cross lingual alignment vs joint training a comparative study and a simple unified framework"):
  with members:
    - "adversarial learning with contextual embeddings for zero resource cross lingual classification and ner"
    - "a survey of cross lingual word embedding models"
    - "bilingual lexicon induction through unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "density matching for bilingual word embedding"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"

Connected through hyperedge HE-3 (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-4 (induced by source paper "context aware machine learning"):
  with members:
    - "deep visual attention prediction"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lstm a search space odyssey"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a structured self attentive sentence embedding"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "xlda cross lingual data augmentation for natural language inference and question answering"):
  with members:
    - "cross lingual language model pretraining"
    - "xnli evaluating cross lingual sentence representations"
    - "universal neural machine translation for extremely low resource languages"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "multilingual extractive reading comprehension by runtime machine translation"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "fine tuned language models for text classification"

Connected through hyperedge HE-6 (induced by source paper "emu enhancing multilingual sentence embeddings with semantic specialization"):
  with members:
    - "sentence bert sentence embeddings using siamese bert networks"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "improving multilingual sentence embedding using bi directional dual encoder with additive margin softmax"
    - "multi lingual common semantic space construction via cluster consistent word embedding"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "unsupervised multilingual word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "cross lingual language model pretraining"):
  with members:
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised cross lingual word embedding by multilingual neural language models"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-8 (induced by source paper "density matching for bilingual word embedding"):
  with members:
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "universal neural machine translation for extremely low resource languages"
    - "near or far wide range zero shot cross lingual dependency parsing"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"

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)