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: "investigating backtranslation in neural machine translation"
  Abstract: A prerequisite for training corpus-based machine translation (MT) systems -- either Statistical MT (SMT) or Neural MT (NMT) -- is the availability of high-quality parallel data. This is arguably more important today than ever before, as NMT has been shown in many studies to outperform SMT, but mostly when large parallel corpora are available; in cases where data is limited, SMT can still outperform NMT. #R##N#Recently researchers have shown that back-translating monolingual data can be used to create synthetic parallel corpora, which in turn can be used in combination with authentic parallel data to train a high-quality NMT system. Given that large collections of new parallel text become available only quite rarely, backtranslation has become the norm when building state-of-the-art NMT systems, especially in resource-poor scenarios. #R##N#However, we assert that there are many unknown factors regarding the actual effects of back-translated data on the translation capabilities of an NMT model. Accordingly, in this work we investigate how using back-translated data as a training corpus -- both as a separate standalone dataset as well as combined with human-generated parallel data -- affects the performance of an NMT model. We use incrementally larger amounts of back-translated data to train a range of NMT systems for German-to-English, and analyse the resulting translation performance.

Connected through hyperedge HE-1 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation"):
  with members:
    - "multilingual denoising pre training for neural machine translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "unsupervised statistical machine translation"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "scaling neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-2 (induced by source paper "evaluating low resource machine translation between chinese and vietnamese with back translation"):
  with members:
    - "combining smt and nmt back translated data for efficient nmt"
    - "a survey of methods to leverage monolingual data in low resource neural machine translation"
    - "simple fusion return of the language model"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "building a neural machine translation system using only synthetic parallel data"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-3 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "a survey of domain adaptation for neural machine translation"
    - "characterizing and avoiding negative transfer"

Connected through hyperedge HE-4 (induced by source paper "cued wmt19 ewc lms"):
  with members:
    - "selective attention for context aware neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "modeling coherence for discourse neural machine translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "improving the transformer translation model with document level context"
    - "simple fusion return of the language model"
    - "the university of cambridge s machine translation systems for wmt18"

Connected through hyperedge HE-5 (induced by source paper "learning credible deep neural networks with rationale regularization"):
  with members:
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "techniques for interpretable machine learning"
    - "know what you don t know unanswerable questions for squad"

Connected through hyperedge HE-6 (induced by source paper "suppressing model overfitting for image super resolution networks"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "blind super resolution with iterative kernel correction"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "data augmentation by pairing samples for images classification"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "style transfer through back translation"

Connected through hyperedge HE-8 (induced by source paper "generating diverse translation by manipulating multi head attention"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "mixture models for diverse machine translation tricks of the trade"
    - "d page diverse paraphrase generation"
    - "multi head attention with disagreement regularization"
    - "convolutional self attention network"
    - "sequence to sequence mixture model for diverse machine translation"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "linguistically informed self attention for semantic role labeling"

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)