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: "transfer learning for low resource neural machine translation"
  Abstract: The encoder-decoder framework for neural machine translation (NMT) has been shown effective in large data scenarios, but is much less effective for low-resource languages. We present a transfer learning method that significantly improves Bleu scores across a range of low-resource languages. Our key idea is to first train a high-resource language pair (the parent model), then transfer some of the learned parameters to the low-resource pair (the child model) to initialize and constrain training. Using our transfer learning method we improve baseline NMT models by an average of 5.6 Bleu on four low-resource language pairs. Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair. Additionally, using the transfer learning model for re-scoring, we can improve the SBMT system by an average of 1.3 Bleu, improving the state-of-the-art on low-resource machine translation.

Connected through hyperedge HE-1 (induced by source paper "a brief survey of multilingual neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "the missing ingredient in zero shot neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"
    - "zero resource multilingual model transfer learning what to share"

Connected through hyperedge HE-2 (induced by source paper "urdu english machine transliteration using neural networks"):
  with members:
    - "how much does tokenization affect neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "beam search strategies for neural machine translation"
    - "six challenges for neural machine translation"
    - "natural language processing state of the art current trends and challenges"
    - "neural machine transliteration preliminary results"
    - "polish to english statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "pre training via leveraging assisting languages and data selection for neural machine translation"):
  with members:
    - "target conditioned sampling optimizing data selection for multilingual neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "addressing word order divergence in multilingual neural machine translation for extremely low resource languages"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (induced by source paper "investigating an approach for low resource language dataset creation curation and classification setswana and sepedi"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "improving short text classification through global augmentation methods"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "enriching word vectors with subword information"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "skin mimo vibration based mimo communication over human skin"):
  with members:
    - "blazeface sub millisecond neural face detection on mobile gpus"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "high dimensional csi acquisition in massive mimo sparsity inspired approaches"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (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-7 (induced by source paper "autogan neural architecture search for generative adversarial networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "controllable artistic text style transfer via shape matching gan"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "enlightengan deep light enhancement without paired supervision"
    - "the gan landscape losses architectures regularization and normalization"
    - "improving mmd gan training with repulsive loss function"
    - "the relativistic discriminator a key element missing from standard gan"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-8 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "mass masked sequence to sequence pre training for language generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"
    - "achieving human parity on automatic chinese to english news 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)