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: "data augmentation for low resource neural machine translation"
  Abstract: The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we propose a novel data augmentation approach that targets low-frequency words by generating new sentence pairs containing rare words in new, synthetically created contexts. Experimental results on simulated low-resource settings show that our method improves translation quality by up to 2.9 BLEU points over the baseline and up to 3.2 BLEU over back-translation.

Connected through hyperedge HE-1 (induced by source paper "snippext semi supervised opinion mining with augmented data"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "voyageur an experiential travel search engine"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "subjective databases"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "scibert a pretrained language model for scientific text"
    - "scibert pretrained contextualized embeddings for scientific text"

Connected through hyperedge HE-2 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "dialog state tracking with reinforced data augmentation"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "user modeling for task oriented dialogues"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"

Connected through hyperedge HE-3 (induced by source paper "abstractive summarization for low resource data using domain transfer and data synthesis"):
  with members:
    - "bottom up abstractive summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep transfer reinforcement learning for text summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "investigating backtranslation in neural machine translation"
    - "multi task neural models for translating between styles within and across languages"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

Connected through hyperedge HE-5 (induced by source paper "rewe regressing word embeddings for regularization of neural machine translation systems"):
  with members:
    - "regularizing neural machine translation by target bidirectional agreement"
    - "training neural machine translation using word embedding based loss"
    - "token level and sequence level loss smoothing for rnn language models"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bag of words as target for neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "robust neural machine translation with doubly adversarial inputs"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "generating natural language adversarial examples"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "training deeper neural machine translation models with transparent attention"
    - "mtnt a testbed for machine translation of noisy text"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "improving back translation with uncertainty based confidence estimation"):
  with members:
    - "openkiwi an open source framework for quality estimation"
    - "investigating backtranslation in neural machine translation"
    - "quantifying uncertainties in natural language processing tasks"
    - "achieving human parity on automatic chinese to english news translation"
    - "confidence modeling for neural semantic parsing"
    - "triangular architecture for rare language translation"
    - "phrase based neural unsupervised machine translation"
    - "modeling uncertainty with hedged instance embedding"

Connected through hyperedge HE-8 (induced by source paper "data diversification an elegant strategy for neural machine translation"):
  with members:
    - "incorporating bert into neural machine translation"
    - "improving neural machine translation robustness via data augmentation beyond back translation"
    - "soft contextual data augmentation for neural machine translation"
    - "pay less attention with lightweight and dynamic convolutions"
    - "the evolved transformer"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"

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)