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: "one sentence one model for neural machine translation"
  Abstract: Neural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.

Connected through hyperedge HE-1 (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"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "instance based model adaptation for direct speech translation"):
  with members:
    - "attention passing models for robust and data efficient end to end speech translation"
    - "non parametric adaptation for neural machine translation"
    - "end to end speech translation with knowledge distillation"
    - "tied multitask learning for neural speech translation"
    - "end to end automatic speech translation of audiobooks"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "the ustc nel speech translation system at iwslt 2018"
    - "leveraging weakly supervised data to improve end to end speech to text translation"

Connected through hyperedge HE-3 (induced by source paper "selecting artificially generated sentences for fine tuning neural machine translation"):
  with members:
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "combining smt and nmt back translated data for efficient nmt"
    - "transductive data selection algorithms for fine tuning neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "the adapt system description for the iwslt 2018 basque to english translation task"
    - "data selection with feature decay algorithms using an approximated target side"
    - "dynamic data selection for neural machine translation"
    - "fast domain adaptation for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "transductive data selection algorithms for fine tuning neural machine translation"):
  with members:
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "a survey of domain adaptation for neural machine translation"
    - "the adapt system description for the iwslt 2018 basque to english translation task"
    - "data selection with feature decay algorithms using an approximated target side"
    - "opennmt open source toolkit for neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "fast domain adaptation for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "a survey of domain adaptation for neural machine translation"):
  with members:
    - "domain generalization via conditional invariant representation"
    - "multi adversarial domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "attention is all you need"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "building a neural machine translation system using only synthetic parallel data"
    - "adversarial discriminative domain adaptation"
    - "multiple source domain adaptation with adversarial training of neural networks"

Connected through hyperedge HE-6 (induced by source paper "retrieval augmented convolutional neural networks for improved robustness against adversarial examples"):
  with members:
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "generating sentences by editing prototypes"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "billion scale similarity search with gpus"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-7 (induced by source paper "freezing subnetworks to analyze domain adaptation in neural machine translation"):
  with members:
    - "extreme adaptation for personalized neural machine translation"
    - "sockeye a toolkit for neural machine translation"
    - "transfer learning for speech recognition on a budget"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "regularization techniques for fine tuning in neural machine translation"
    - "transfer learning across low resource related languages for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "guiding neural machine translation with retrieved translation pieces"):
  with members:
    - "learning to remember rare events"
    - "improving lexical choice in neural machine translation"
    - "search engine guided non parametric neural machine translation"
    - "six challenges for neural machine translation"
    - "neural machine translation by minimising the bayes risk with respect to syntactic translation lattices"
    - "neural machine translation with external phrase memory"
    - "edinburgh neural machine translation systems for wmt 16"
    - "incorporating discrete translation lexicons into neural machine translation"

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)