human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "microsoft translator at wmt 2019 towards large scale document level neural machine translation"):
  Abstract: This paper describes the Microsoft Translator submissions to the WMT19 news translation shared task for English-German. Our main focus is document-level neural machine translation with deep transformer models. We start with strong sentence-level baselines, trained on large-scale data created via data-filtering and noisy back-translation and find that back-translation seems to mainly help with translationese input. We explore fine-tuning techniques, deeper models and different ensembling strategies to counter these effects. Using document boundaries present in the authentic and synthetic parallel data, we create sequences of up to 1000 subword segments and train transformer translation models. We experiment with data augmentation techniques for the smaller authentic data with document-boundaries and for larger authentic data without boundaries. We further explore multi-task training for the incorporation of document-level source language monolingual data via the BERT-objective on the encoder and two-pass decoding for combinations of sentence-level and document-level systems. Based on preliminary human evaluation results, evaluators strongly prefer the document-level systems over our comparable sentence-level system. The document-level systems also seem to score higher than the human references in source-based direct assessment.

Includes members:
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incorporating bert into neural machine translation")
  Member "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cued wmt19 ewc lms")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "a call for clarity in reporting bleu scores":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "dual conditional cross entropy filtering of noisy parallel corpora":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "membership inference attacks on sequence to sequence models is my data in your machine translation system")
  Member "understanding back translation at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "neural machine translation with extended context":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual denoising pre training for neural machine translation")

Question: Please tell me which class the source paper 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)