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 "ordering matters word ordering aware unsupervised nmt"):
  Abstract: Denoising-based Unsupervised Neural Machine Translation (U-NMT) models typically employ denoising strategy at the encoder module to prevent the model from memorizing the input source sentence. Specifically, given an input sentence of length n, the model applies n/2 random swaps between consecutive words and trains the denoising-based U-NMT model. Though effective, applying denoising strategy on every sentence in the training data leads to uncertainty in the model thereby, limiting the benefits from the denoising-based U-NMT model. In this paper, we propose a simple fine-tuning strategy where we fine-tune the trained denoising-based U-NMT system without the denoising strategy. The input sentences are presented as is i.e., without any shuffling noise added. We observe significant improvements in translation performance on many language pairs from our fine-tuning strategy. Our analysis reveals that our proposed models lead to increase in higher n-gram BLEU score compared to the denoising U-NMT models.

Includes members:
  Member "an effective approach to unsupervised machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a multilingual view of unsupervised machine translation")
  Member "extract and edit an alternative to back translation for unsupervised neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "improving unsupervised word by word translation with language model and denoising autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "unsupervised statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation")
  Member "addressing word order divergence in multilingual neural machine translation for extremely low resource languages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "on difficulties of cross lingual transfer with order differences a case study on dependency parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "unsupervised neural machine translation with weight sharing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "mtnt a testbed for machine translation of noisy text":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "findings of the first shared task on machine translation robustness")

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)