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 "debiasing word embeddings improves multimodal machine translation"):
  Abstract: In recent years, pretrained word embeddings have proved useful for multimodal neural machine translation (NMT) models to address the shortage of available datasets. However, the integration of pretrained word embeddings has not yet been explored extensively. Further, pretrained word embeddings in high dimensional spaces have been reported to suffer from the hubness problem. Although some debiasing techniques have been proposed to address this problem for other natural language processing tasks, they have seldom been studied for multimodal NMT models. In this study, we examine various kinds of word embeddings and introduce two debiasing techniques for three multimodal NMT models and two language pairs -- English-German translation and English-French translation. With our optimal settings, the overall performance of multimodal models was improved by up to +1.93 BLEU and +2.02 METEOR for English-German translation and +1.73 BLEU and +0.95 METEOR for English-French translation.

Includes members:
  Member "multimodal machine translation with embedding prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic fusion attentional language model for neural machine translation")
  Member "lium cvc submissions for wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial reconstruction for multi modal machine translation")
  Member "cuni system for the wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two headed monster and crossed co attention networks")
  Member "when and why are pre trained word embeddings useful for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distill adapt distill training small in domain models for neural machine translation")
  Member "a visual attention grounding neural model for multimodal machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a formal analysis of multimodal referring strategies under common ground")
  Member "imagination improves multimodal translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "countering language drift via visual grounding")
  Member "nmtpy a flexible toolkit for advanced neural machine translation systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "looking enhances listening recovering missing speech using images")
  Member "doubly attentive decoder for multi modal neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probing contextualized sentence representations with visual awareness")

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)