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 "transfer learning across languages from someone else s nmt model"):
  Abstract: Neural machine translation is demanding in terms of training time, hardware resources, size, and quantity of parallel sentences. We propose a simple transfer learning method to recycle already trained models for different language pairs with no need for modifications in model architecture, hyper-parameters, or vocabulary. We achieve better translation quality and shorter convergence times than when training from random initialization. To show the applicability of our method, we recycle a Transformer model trained by different researchers for translating English-to-Czech and used it to seed models for seven language pairs. Our translation models are significantly better even when the re-used model's language pair is not linguistically related to the child language pair, especially for low-resource languages. Our approach needs only one pretrained model for all transferring to all various languages pairs. Additionally, we improve this approach with a simple vocabulary transformation. We analyze the behavior of transfer learning to understand the gains from unrelated languages.

Includes members:
  Member "freezing subnetworks to analyze domain adaptation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "simple scalable adaptation for neural machine translation")
  Member "trivial transfer learning for low resource neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "universal neural machine translation for extremely low resource languages":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "a survey on deep transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "a call for clarity in reporting bleu scores":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "rapid adaptation of neural machine translation to new languages":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "a neural interlingua for multilingual machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "phrase based neural unsupervised machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual denoising pre training for 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)