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 pretraining for sequence to sequence learning"
  Abstract: This work presents a general unsupervised learning method to improve the accuracy of sequence to sequence (seq2seq) models. In our method, the weights of the encoder and decoder of a seq2seq model are initialized with the pretrained weights of two language models and then fine-tuned with labeled data. We apply this method to challenging benchmarks in machine translation and abstractive summarization and find that it significantly improves the subsequent supervised models. Our main result is that pretraining improves the generalization of seq2seq models. We achieve state-of-the art results on the WMT English$\rightarrow$German task, surpassing a range of methods using both phrase-based machine translation and neural machine translation. Our method achieves a significant improvement of 1.3 BLEU from the previous best models on both WMT'14 and WMT'15 English$\rightarrow$German. We also conduct human evaluations on abstractive summarization and find that our method outperforms a purely supervised learning baseline in a statistically significant manner.

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"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-3 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks"):
  with members:
    - "attentive mimicking better word embeddings by attending to informative contexts"
    - "phonology augmented statistical framework for machine transliteration using limited linguistic resources"
    - "chinese japanese unsupervised neural machine translation using sub character level information"
    - "neural machine translation of logographic languages using sub character level information"
    - "generalizing word embeddings using bag of subwords"
    - "dual long short term memory networks for sub character representation learning"
    - "extrapolation in nlp"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "unsupervised domain adaptation for neural machine translation with iterative back translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "cross lingual language model pretraining"
    - "a survey of domain adaptation for neural machine translation"
    - "unsupervised neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "on architectures for including visual information in neural language models for image description"):
  with members:
    - "transfer learning from language models to image caption generators better models may not transfer better"
    - "visual entailment a novel task for fine grained image understanding"
    - "do better imagenet models transfer better"
    - "quantifying the amount of visual information used by neural caption generators"
    - "pre gen metrics predicting caption quality metrics without generating captions"
    - "object counts bringing explicit detections back into image captioning"
    - "the memad submission to the wmt18 multimodal translation task"
    - "grounded textual entailment"

Connected through hyperedge HE-6 (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"
    - "camembert a tasty french language model"
    - "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"

Connected through hyperedge HE-7 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "mass masked sequence to sequence pre training for language generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-8 (induced by source paper "denoising based sequence to sequence pre training for text generation"):
  with members:
    - "pre trained language model representations for language generation"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

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)