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: "systran s pure neural machine translation systems"
  Abstract: Since the first online demonstration of Neural Machine Translation (NMT) by LISA, NMT development has recently moved from laboratory to production systems as demonstrated by several entities announcing roll-out of NMT engines to replace their existing technologies. NMT systems have a large number of training configurations and the training process of such systems is usually very long, often a few weeks, so role of experimentation is critical and important to share. In this work, we present our approach to production-ready systems simultaneously with release of online demonstrators covering a large variety of languages (12 languages, for 32 language pairs). We explore different practical choices: an efficient and evolutive open-source framework; data preparation; network architecture; additional implemented features; tuning for production; etc. We discuss about evaluation methodology, present our first findings and we finally outline further work. #R##N#Our ultimate goal is to share our expertise to build competitive production systems for "generic" translation. We aim at contributing to set up a collaborative framework to speed-up adoption of the technology, foster further research efforts and enable the delivery and adoption to/by industry of use-case specific engines integrated in real production workflows. Mastering of the technology would allow us to build translation engines suited for particular needs, outperforming current simplest/uniform systems.

Connected through hyperedge HE-1 (induced by source paper "marathi to english neural machine translation with near perfect corpus and transformers"):
  with members:
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "achieving human parity on automatic chinese to english news translation"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "toward multilingual neural machine translation with universal encoder and decoder"

Connected through hyperedge HE-2 (induced by source paper "consistency by agreement in zero shot neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "tensor2tensor for neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "style transfer through back translation"
    - "large scale distributed neural network training through online distillation"
    - "a neural interlingua for multilingual machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-3 (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"
    - "pre translation for neural machine translation"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"

Connected through hyperedge HE-4 (induced by source paper "code switching for enhancing nmt with pre specified translation"):
  with members:
    - "on the alignment problem in multi head attention based neural machine translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "get to the point summarization with pointer generator networks"
    - "searching for activation functions"
    - "pointing the unknown words"

Connected through hyperedge HE-5 (induced by source paper "ntt s machine translation systems for wmt19 robustness task"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "findings of the first shared task on machine translation robustness"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "mtnt a testbed for machine translation of noisy text"
    - "neural machine translation with extended context"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "an analysis of source side grammatical errors in nmt"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "reference less quality estimation of text simplification systems"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "neural machine translation of text from non native speakers"
    - "understanding back translation at scale"

Connected through hyperedge HE-7 (induced by source paper "online learning for effort reduction in interactive neural machine translation"):
  with members:
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "online learning for neural machine translation post editing"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "trainable greedy decoding for neural machine translation"
    - "massive exploration of neural machine translation architectures"

Connected through hyperedge HE-8 (induced by source paper "xnmt the extensible neural machine translation toolkit"):
  with members:
    - "linguistic unit discovery from multi modal inputs in unwritten languages summary of the speaking rosetta jsalt 2017 workshop"
    - "attention is all you need"
    - "using the output embedding to improve language models"
    - "effective approaches to attention based neural machine translation"
    - "minimum risk training for neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "sequence level training with recurrent neural networks"
    - "semi supervised sequence learning"

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)