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 "do all roads lead to rome understanding the role of initialization in iterative back translation"):
  Abstract: Back-translation provides a simple yet effective approach to exploit monolingual corpora in Neural Machine Translation (NMT). Its iterative variant, where two opposite NMT models are jointly trained by alternately using a synthetic parallel corpus generated by the reverse model, plays a central role in unsupervised machine translation. In order to start producing sound translations and provide a meaningful training signal to each other, existing approaches rely on either a separate machine translation system to warm up the iterative procedure, or some form of pre-training to initialize the weights of the model. In this paper, we analyze the role that such initialization plays in iterative back-translation. Is the behavior of the final system heavily dependent on it? Or does iterative back-translation converge to a similar solution given any reasonable initialization? Through a series of empirical experiments over a diverse set of warmup systems, we show that, although the quality of the initial system does affect final performance, its effect is relatively small, as iterative back-translation has a strong tendency to convergence to a similar solution. As such, the margin of improvement left for the initialization method is narrow, suggesting that future research should focus more on improving the iterative mechanism itself.

Includes members:
  Member "multilingual denoising pre training for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a multilingual view of unsupervised machine translation")
  Member "using monolingual data in neural machine translation a systematic study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational template machine for data to text generation")
  Member "an effective approach to unsupervised machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multilingual view of unsupervised machine translation")
  Member "mass masked sequence to sequence pre training for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "investigating backtranslation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluating low resource machine translation between chinese and vietnamese with back translation")
  Member "unsupervised statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mass masked sequence to sequence pre training for language generation")
  Member "unsupervised neural machine translation initialized by unsupervised statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "explicit cross lingual pre training for unsupervised machine translation")
  Member "scaling neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")

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)