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 representation learning at scale"
  Abstract: This paper shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of cross-lingual transfer tasks. We train a Transformer-based masked language model on one hundred languages, using more than two terabytes of filtered CommonCrawl data. Our model, dubbed XLM-R, significantly outperforms multilingual BERT (mBERT) on a variety of cross-lingual benchmarks, including +13.8% average accuracy on XNLI, +12.3% average F1 score on MLQA, and +2.1% average F1 score on NER. XLM-R performs particularly well on low-resource languages, improving 11.8% in XNLI accuracy for Swahili and 9.2% for Urdu over the previous XLM model. We also present a detailed empirical evaluation of the key factors that are required to achieve these gains, including the trade-offs between (1) positive transfer and capacity dilution and (2) the performance of high and low resource languages at scale. Finally, we show, for the first time, the possibility of multilingual modeling without sacrificing per-language performance; XLM-Ris very competitive with strong monolingual models on the GLUE and XNLI benchmarks. We will make XLM-R code, data, and models publicly available.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "q bert hessian based ultra low precision quantization of bert"
    - "spanbert improving pre training by representing and predicting spans"

Connected through hyperedge HE-2 (induced by source paper "xpersona evaluating multilingual personalized chatbot"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "towards a human like open domain chatbot"
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "code switched language models using neural based synthetic data from parallel sentences"
    - "camembert a tasty french language model"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-3 (induced by source paper "contextual lensing of universal sentence representations"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "ccmatrix mining billions of high quality parallel sentences on the web"
    - "parameter efficient transfer learning for nlp"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "learning to few shot learn across diverse natural language classification tasks"
    - "zero shot text classification with generative language models"

Connected through hyperedge HE-4 (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"
    - "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"
    - "pretrained language models for document level neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "arabert transformer based model for arabic language understanding"):
  with members:
    - "towards a human like open domain chatbot"
    - "neural arabic question answering"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "camembert a tasty french language model"
    - "albert a lite bert for self supervised learning of language representations"
    - "arsentd lev a multi topic corpus for target based sentiment analysis in arabic levantine tweets"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-6 (induced by source paper "aligning the pretraining and finetuning objectives of language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "ernie 2 0 a continual pre training framework for language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "tinybert distilling bert for natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "on the importance of word order information in cross lingual sequence labeling"):
  with members:
    - "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"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "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-8 (induced by source paper "phobert pre trained language models for vietnamese"):
  with members:
    - "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task"
    - "camembert a tasty french language model"
    - "cross lingual language model pretraining"
    - "roberta a robustly optimized bert pretraining approach"
    - "bertje a dutch bert model"
    - "xnli evaluating cross lingual sentence representations"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "vncorenlp a vietnamese natural language processing toolkit"

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)