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: "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
  Abstract: This paper describes the Microsoft submission to the WMT2018 news translation shared task. We participated in one language direction -- English-German. Our system follows current best-practice and combines state-of-the-art models with new data filtering (dual conditional cross-entropy filtering) and sentence weighting methods. We trained fairly standard Transformer-big models with an updated version of Edinburgh's training scheme for WMT2017 and experimented with different filtering schemes for Paracrawl. According to automatic metrics (BLEU) we reached the highest score for this subtask with a nearly 2 BLEU point margin over the next strongest system. Based on human evaluation we ranked first among constrained systems. We believe this is mostly caused by our data filtering/weighting regime.

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 "ape at scale and its implications on mt evaluation biases"):
  with members:
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "self attention with relative position representations"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "the university of cambridge s machine translation systems for wmt18"
    - "a call for clarity in reporting bleu scores"
    - "joint training for neural machine translation models with monolingual data"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-3 (induced by source paper "depth growing for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"

Connected through hyperedge HE-4 (induced by source paper "simple and effective noisy channel modeling for neural machine translation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "achieving human parity on automatic chinese to english news translation"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "adaptive input representations for neural language modeling"
    - "understanding back translation at scale"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "the university of edinburgh s submissions to the wmt19 news translation task"):
  with members:
    - "cross lingual language model pretraining"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a call for clarity in reporting bleu scores"
    - "understanding back translation at scale"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "microsoft research asia s systems for wmt19"):
  with members:
    - "soft contextual data augmentation for neural machine translation"
    - "unsupervised pivot translation for distant languages"
    - "mass masked sequence to sequence pre training for language generation"
    - "achieving human parity on automatic chinese to english news translation"
    - "a study of reinforcement learning for neural machine translation"
    - "efficient neural architecture search via parameter sharing"
    - "understanding back translation at scale"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

Connected through hyperedge HE-7 (induced by source paper "robust neural machine translation for clean and noisy speech transcripts"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "punctuation prediction model for conversational speech"
    - "attention is all you need"
    - "mitigating the impact of speech recognition errors on chatbot using sequence to sequence model"
    - "synthetic and natural noise both break neural machine translation"
    - "assessing the tolerance of neural machine translation systems against speech recognition errors"

Connected through hyperedge HE-8 (induced by source paper "unbabel s submission to the wmt2019 ape shared task bert based encoder decoder for automatic post editing"):
  with members:
    - "a simple and effective approach to automatic post editing with transfer learning"
    - "multi task deep neural networks for natural language understanding"
    - "unbabel s participation in the wmt19 translation quality estimation shared task"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "opennmt open source toolkit for neural machine translation"
    - "using the output embedding to improve language models"

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)