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: "camembert a tasty french language model"
  Abstract: Pretrained language models are now ubiquitous in Natural Language Processing. Despite their success, most available models have either been trained on English data or on the concatenation of data in multiple languages. This makes practical use of such models—in all languages except English—very limited. Aiming to address this issue for French, we release CamemBERT, a French version of the Bi-directional Encoders for Transformers (BERT). We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference. CamemBERT improves the state of the art for most of the tasks considered. We release the pretrained model for CamemBERT hoping to foster research and downstream applications for French NLP.

Connected through hyperedge HE-1 (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"
    - "unsupervised cross lingual representation learning at scale"
    - "zero shot cross lingual dialogue systems with transferable latent variables"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (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"
    - "unsupervised cross lingual representation learning at scale"
    - "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-3 (induced by source paper "what the mask making sense of language specific bert models"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "binary and multitask classification model for dutch anaphora resolution die dat prediction"
    - "robbert a dutch roberta based language model"
    - "a primer in bertology what we know about how bert works"
    - "albert a lite bert for self supervised learning of language representations"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"
    - "flaubert unsupervised language model pre training for french"
    - "multilingual is not enough bert for finnish"

Connected through hyperedge HE-4 (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"
    - "unsupervised cross lingual representation learning at scale"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "robbert a dutch roberta based language model"):
  with members:
    - "binary and multitask classification model for dutch anaphora resolution die dat prediction"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "is multilingual bert fluent in language generation"
    - "albert a lite bert for self supervised learning of language representations"
    - "the merits of universal language model fine tuning for small datasets a case with dutch book reviews"
    - "how multilingual is multilingual bert"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-6 (induced by source paper "fquad french question answering dataset"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "multilingual question answering from formatted text applied to conversational agents"
    - "mlqa evaluating cross lingual extractive question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"

Connected through hyperedge HE-7 (induced by source paper "flaubert unsupervised language model pre training for french"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "reducing transformer depth on demand with structured dropout"
    - "very deep self attention networks for end to end speech recognition"
    - "unsupervised cross lingual representation learning at scale"
    - "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"
    - "paws paraphrase adversaries from word scrambling"

Connected through hyperedge HE-8 (induced by source paper "massive vs curated word embeddings for low resourced languages the case of yoruba and twi"):
  with members:
    - "cross lingual language model pretraining"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "enriching word vectors with subword information"

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)