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: "neural machine translation with pivot languages"
  Abstract: While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages, the source-to-pivot and pivot-to-target translation models are usually independently trained. In this work, we introduce a joint training algorithm for pivot-based neural machine translation. We propose three methods to connect the two models and enable them to interact with each other during training. Experiments on Europarl and WMT corpora show that joint training of source-to-pivot and pivot-to-target models leads to significant improvements over independent training across various languages.

Connected through hyperedge HE-1 (induced by source paper "ouroboros on accelerating training of transformer based language models"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "decoupled parallel backpropagation with convergence guarantee"
    - "training neural networks using features replay"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "a deep generative framework for paraphrase generation"

Connected through hyperedge HE-2 (induced by source paper "improved zero shot neural machine translation via ignoring spurious correlations"):
  with members:
    - "cross lingual language model pretraining"
    - "the missing ingredient in zero shot neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "zero resource neural machine translation with multi agent communication game"
    - "meta learning for low resource neural machine translation"
    - "contextual parameter generation for universal neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "zero shot dual machine translation"

Connected through hyperedge HE-3 (induced by source paper "translating translationese a two step approach to unsupervised machine translation"):
  with members:
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised neural machine translation with weight sharing"
    - "a teacher student framework for zero resource neural machine translation"
    - "attention is all you need"
    - "effective strategies in zero shot neural machine translation"
    - "unsupervised neural machine translation"
    - "six challenges for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "meta learning for low resource neural machine translation"):
  with members:
    - "universal neural machine translation for extremely low resource languages"
    - "zero resource neural machine translation with multi agent communication game"
    - "unsupervised neural machine translation with weight sharing"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised neural machine translation with weight sharing"):
  with members:
    - "unsupervised machine translation using monolingual corpora only"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "improving neural machine translation with conditional sequence generative adversarial nets"
    - "multi channel encoder for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "deep neural machine translation with linear associative unit"
    - "disan directional self attention network for rnn cnn free language understanding"

Connected through hyperedge HE-6 (induced by source paper "word subword or character an empirical study of granularity in chinese english nmt"):
  with members:
    - "neural system combination for machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "bridging neural machine translation and bilingual dictionaries"
    - "interactive attention for neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "modeling coverage for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-7 (induced by source paper "building a neural machine translation system using only synthetic parallel data"):
  with members:
    - "zero resource translation with multi lingual neural machine translation"
    - "bridging neural machine translation and bilingual dictionaries"
    - "semi supervised learning for neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "edinburgh neural machine translation systems for wmt 16"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "a teacher student framework for zero resource neural machine translation"):
  with members:
    - "zero resource translation with multi lingual neural machine translation"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "transfer learning for low resource neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "minimum risk training for neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "sequence level training with recurrent neural networks"

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)