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: "pre translation for neural machine translation"
  Abstract: Recently, the development of neural machine translation (NMT) has significantly improved the translation quality of automatic machine translation. While most sentences are more accurate and fluent than translations by statistical machine translation (SMT)-based systems, in some cases, the NMT system produces translations that have a completely different meaning. This is especially the case when rare words occur. #R##N#When using statistical machine translation, it has already been shown that significant gains can be achieved by simplifying the input in a preprocessing step. A commonly used example is the pre-reordering approach. #R##N#In this work, we used phrase-based machine translation to pre-translate the input into the target language. Then a neural machine translation system generates the final hypothesis using the pre-translation. Thereby, we use either only the output of the phrase-based machine translation (PBMT) system or a combination of the PBMT output and the source sentence. #R##N#We evaluate the technique on the English to German translation task. Using this approach we are able to outperform the PBMT system as well as the baseline neural MT system by up to 2 BLEU points. We analyzed the influence of the quality of the initial system on the final result.

Connected through hyperedge HE-1 (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"
    - "investigating backtranslation in 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"

Connected through hyperedge HE-2 (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"
    - "data augmentation for low resource neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"

Connected through hyperedge HE-4 (induced by source paper "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "sentence rewriting for semantic parsing"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "end to end neural coreference resolution"
    - "quickedit editing text translations by crossing words out"

Connected through hyperedge HE-5 (induced by source paper "synchronous bidirectional neural machine translation"):
  with members:
    - "asynchronous bidirectional decoding for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "modeling past and future for neural machine translation"
    - "learning to decode for future success"

Connected through hyperedge HE-6 (induced by source paper "automatic post editing for machine translation"):
  with members:
    - "active learning for interactive neural machine translation of data streams"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "convolutional sequence to sequence learning"
    - "ensembling factored neural machine translation models for automatic post editing and quality estimation"
    - "systran s pure neural machine translation systems"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"

Connected through hyperedge HE-7 (induced by source paper "learning to copy for automatic post editing"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "cross lingual language model pretraining"
    - "escape a large scale synthetic corpus for automatic post editing"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "modeling localness for self attention networks"
    - "a shared attention mechanism for interpretation of neural automatic post editing systems"
    - "thumt an open source toolkit for neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "think again networks and the delta loss"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "direct output connection for a high rank language model"
    - "trellis networks for sequence modeling"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural system combination for machine translation"
    - "attention is all you need"

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)