human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "is neural machine translation ready for deployment a case study on 30 translation directions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cued wmt19 ewc lms")
  Member "implicit distortion and fertility models for attention based encoder decoder nmt model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "controlling utterance length in nmt based word segmentation with attention")
  Member "neural machine translation with supervised attention":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an empirical study of spatial attention mechanisms in deep networks")
  Member "interactive attention for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving multi head attention with capsule networks")
  Member "a coverage embedding model for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "apply chinese radicals into neural machine translation deeper than character level")
  Member "semi supervised learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "fully character level neural machine translation without explicit segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a brief survey of multilingual neural machine translation")

Question: Please tell me which class the source paper 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)