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: "beto bentz becas the surprising cross lingual effectiveness of bert"
  Abstract: Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.

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 "bert can see out of the box on the cross modal transferability of text representations"):
  with members:
    - "deep bayesian network for visual question generation"
    - "incorporating visual semantics into sentence representations within a grounded space"
    - "visualizing and measuring the geometry of bert"
    - "lxmert learning cross modality encoder representations from transformers"
    - "bert rediscovers the classical nlp pipeline"
    - "context aware zero shot learning for object recognition"
    - "fusion of detected objects in text for visual question answering"
    - "supervised multimodal bitransformers for classifying images and text"

Connected through hyperedge HE-3 (induced by source paper "zero shot cross lingual transfer with meta learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "x wikire a large multilingual resource for relation extraction as machine comprehension"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "on first order meta learning algorithms"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-4 (induced by source paper "on the importance of word order information in cross lingual sequence labeling"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "incorporating word and subword units in unsupervised machine translation using language model rescoring"
    - "attention informed mixed language training for zero shot cross lingual task oriented dialogue systems"
    - "how multilingual is multilingual bert"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "self attention with relative position representations"
    - "universal neural machine translation for extremely low resource languages"

Connected through hyperedge HE-5 (induced by source paper "sensitive data detection and classification in spanish clinical text experiments with bert"):
  with members:
    - "passage re ranking with bert"
    - "unsupervised cross lingual representation learning at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "text summarization with pretrained encoders"
    - "roberta a robustly optimized bert pretraining approach"
    - "a deep learning architecture for de identification of patient notes implementation and evaluation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "x stance a multilingual multi target dataset for stance detection"):
  with members:
    - "allennlp a deep semantic natural language processing platform"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stance detection with bidirectional conditional encoding"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-7 (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"
    - "bilingual lexicon induction with semi supervision in non isometric embedding spaces"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"

Connected through hyperedge HE-8 (induced by source paper "75 languages 1 model parsing universal dependencies universally"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "evaluating contextualized embeddings on 54 languages in pos tagging lemmatization and dependency parsing"
    - "polyglot contextual representations improve crosslingual transfer"
    - "visualizing attention in transformer based language representation models"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "parameter sharing between dependency parsers for related languages"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

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)