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: "unsupervised cross lingual word embedding by multilingual neural language models"
  Abstract: We propose an unsupervised method to obtain cross-lingual embeddings without any parallel data or pre-trained word embeddings. The proposed model, which we call multilingual neural language models, takes sentences of multiple languages as an input. The proposed model contains bidirectional LSTMs that perform as forward and backward language models, and these networks are shared among all the languages. The other parameters, i.e. word embeddings and linear transformation between hidden states and outputs, are specific to each language. The shared LSTMs can capture the common sentence structure among all languages. Accordingly, word embeddings of each language are mapped into a common latent space, making it possible to measure the similarity of words across multiple languages. We evaluate the quality of the cross-lingual word embeddings on a word alignment task. Our experiments demonstrate that our model can obtain cross-lingual embeddings of much higher quality than existing unsupervised models when only a small amount of monolingual data (i.e. 50k sentences) are available, or the domains of monolingual data are different across languages.

Connected through hyperedge HE-1 (induced by source paper "multilingual denoising pre training for neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "pre trained language model representations for language generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (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"
    - "learning word vectors for 157 languages"
    - "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-3 (induced by source paper "low resource parsing with crosslingual contextualized representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "polyglot contextual representations improve crosslingual transfer"
    - "linguistic knowledge and transferability of contextual representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "parameter sharing between dependency parsers for related languages"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (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"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "character level language modeling with deeper self attention"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

Connected through hyperedge HE-5 (induced by source paper "sentence transition matrix an efficient approach that preserves sentence semantics"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "unsupervised cross lingual transfer of word embedding spaces"
    - "paraphrase thought sentence embedding module imitating human language recognition"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "unsupervised neural machine translation"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-6 (induced by source paper "lost in evaluation misleading benchmarks for bilingual dictionary induction"):
  with members:
    - "don t forget the long tail a comprehensive analysis of morphological generalization in bilingual lexicon induction"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "an iterative closest point method for unsupervised word translation"
    - "learning multilingual word embeddings in latent metric space a geometric approach"
    - "unimorph 2 0 universal morphology"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"

Connected through hyperedge HE-7 (induced by source paper "city2city translating place representations across cities"):
  with members:
    - "deepmove learning place representations through large scale movement data"
    - "unsupervised statistical machine translation"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "unsupervised neural machine translation"
    - "word translation without parallel data"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "aligning very small parallel corpora using cross lingual word embeddings and a monogamy objective"):
  with members:
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "associative domain adaptation"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "adam a method for stochastic optimization"

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)