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: "implicit distortion and fertility models for attention based encoder decoder nmt model"
  Abstract: Neural machine translation has shown very promising results lately. Most NMT models follow the encoder-decoder framework. To make encoder-decoder models more flexible, attention mechanism was introduced to machine translation and also other tasks like speech recognition and image captioning. We observe that the quality of translation by attention-based encoder-decoder can be significantly damaged when the alignment is incorrect. We attribute these problems to the lack of distortion and fertility models. Aiming to resolve these problems, we propose new variations of attention-based encoder-decoder and compare them with other models on machine translation. Our proposed method achieved an improvement of 2 BLEU points over the original attention-based encoder-decoder.

Connected through hyperedge HE-1 (induced by source paper "controlling utterance length in nmt based word segmentation with attention"):
  with members:
    - "attention is not not explanation"
    - "attention is not explanation"
    - "tied multitask learning for neural speech translation"
    - "learning to segment inputs for nmt favors character level processing"
    - "low resource speech to text translation"
    - "on the alignment problem in multi head attention based neural machine translation"
    - "pre training on high resource speech recognition improves low resource speech to text translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-2 (induced by source paper "phrase table as recommendation memory for neural machine translation"):
  with members:
    - "translating phrases in neural machine translation"
    - "memory augmented neural 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"
    - "vocabulary manipulation for neural machine translation"
    - "a coverage embedding model for neural machine translation"
    - "the amu uedin submission to the wmt16 news translation task attention based nmt models as feature functions in phrase based smt"

Connected through hyperedge HE-3 (induced by source paper "neural machine translation with key value memory augmented attention"):
  with members:
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "memory augmented neural machine translation"
    - "search engine guided non parametric neural machine translation"
    - "neural machine translation advised by statistical machine translation"
    - "memory enhanced decoder for neural machine translation"
    - "coverage embedding models for neural machine translation"
    - "interactive attention for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "neural machine translation advised by statistical machine translation"):
  with members:
    - "character based neural machine translation"
    - "context gates for neural machine translation"
    - "neural machine translation with reconstruction"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with external phrase memory"
    - "syntactically guided neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "bridging neural machine translation and bilingual dictionaries"):
  with members:
    - "character based neural machine translation"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "neural machine translation with supervised attention"
    - "interactive attention for neural machine translation"
    - "a coverage embedding model for neural machine translation"
    - "semi supervised learning for neural machine translation"
    - "fully character level neural machine translation without explicit segmentation"
    - "incorporating structural alignment biases into an attentional neural translation model"

Connected through hyperedge HE-6 (induced by source paper "learning for biomedical information extraction methodological review of recent advances"):
  with members:
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "attention based convolutional neural network for machine comprehension"
    - "improved relation classification by deep recurrent neural networks with data augmentation"
    - "toward mention detection robustness with recurrent neural networks"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "combining neural networks and log linear models to improve relation extraction"

Connected through hyperedge HE-7 (induced by source paper "neural machine translation with supervised attention"):
  with members:
    - "a survey of word reordering in statistical machine translation computational models and language phenomena"
    - "attention correctness in neural image captioning"
    - "guided alignment training for topic aware neural machine translation"
    - "modeling coverage for neural machine translation"
    - "supervised attentions for neural machine translation"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "incorporating discrete translation lexicons into neural machine translation"
    - "neural reranking improves subjective quality of machine translation naist at wat2015"

Connected through hyperedge HE-8 (induced by source paper "interactive attention for neural machine translation"):
  with members:
    - "memory enhanced decoder for neural machine translation"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with external phrase memory"
    - "incorporating structural alignment biases into an attentional neural translation model"
    - "effective approaches to attention based neural machine translation"
    - "neural transformation machine a new architecture for sequence to sequence learning"
    - "minimum risk training for neural machine translation"
    - "neural turing machines"

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)