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: "bridging neural machine translation and bilingual dictionaries"
  Abstract: Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs. However, it remains a challenging problem how to integrate NMT with a bilingual dictionary which mainly contains words rarely or never seen in the bilingual training data. In this paper, we propose two methods to bridge NMT and the bilingual dictionaries. The core idea behind is to design novel models that transform the bilingual dictionaries into adequate sentence pairs, so that NMT can distil latent bilingual mappings from the ample and repetitive phenomena. One method leverages a mixed word/character model and the other attempts at synthesizing parallel sentences guaranteeing massive occurrence of the translation lexicon. Extensive experiments demonstrate that the proposed methods can remarkably improve the translation quality, and most of the rare words in the test sentences can obtain correct translations if they are covered by the dictionary.

Connected through hyperedge HE-1 (induced by source paper "neural machine translation with noisy lexical constraints"):
  with members:
    - "non parametric adaptation for neural machine translation"
    - "training neural machine translation to apply terminology constraints"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "quickedit editing text translations by crossing words out"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "merging external bilingual pairs into neural machine translation"):
  with members:
    - "code switching for enhancing nmt with pre specified translation"
    - "scaling neural machine translation"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "translating phrases in neural machine translation"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-3 (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"
    - "implicit distortion and fertility models for attention based encoder decoder nmt model"
    - "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-4 (induced by source paper "using deep neural networks to translate multi lingual threat intelligence"):
  with members:
    - "thinking fast and slow combining vector spaces and knowledge graphs"
    - "neural machine translation of rare words with subword units"
    - "embedding word similarity with neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "translating phrases in neural machine translation"):
  with members:
    - "neural system combination for machine translation"
    - "towards string to tree neural machine translation"
    - "modeling source syntax for neural machine translation"
    - "character based neural machine translation"
    - "pointing the unknown words"
    - "neural machine translation advised by statistical machine translation"
    - "tree to sequence attentional neural machine translation"
    - "neural machine translation with reconstruction"

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"
    - "interactive attention for neural machine translation"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "modeling coverage for neural machine translation"
    - "neural machine translation with pivot languages"
    - "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"
    - "neural machine translation with pivot languages"
    - "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 "neural system combination for machine translation"):
  with members:
    - "zero resource translation with multi lingual neural machine translation"
    - "pre translation for neural machine translation"
    - "interactive attention for neural machine translation"
    - "modeling coverage 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"
    - "semi supervised learning for neural machine translation"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "multi source neural 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)