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 "multimodal machine translation with embedding prediction"):
  Abstract: Multimodal machine translation is an attractive application of neural machine translation (NMT). It helps computers to deeply understand visual objects and their relations with natural languages. However, multimodal NMT systems suffer from a shortage of available training data, resulting in poor performance for translating rare words. In NMT, pretrained word embeddings have been shown to improve NMT of low-resource domains, and a search-based approach is proposed to address the rare word problem. In this study, we effectively combine these two approaches in the context of multimodal NMT and explore how we can take full advantage of pretrained word embeddings to better translate rare words. We report overall performance improvements of 1.24 METEOR and 2.49 BLEU and achieve an improvement of 7.67 F-score for rare word translation.

Includes members:
  Member "von mises fisher loss for training sequence to sequence models with continuous outputs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation")
  Member "cuni system for the wmt18 multimodal translation task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "distill adapt distill training small in domain models for neural machine translation")
  Member "learning word vectors for 157 languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "imagination improves multimodal translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-7 (induced by source paper "probing contextualized sentence representations with visual awareness")
  Member "all but the top simple and effective postprocessing for word representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")

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)