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 "the missing ingredient in zero shot neural machine translation"):
  Abstract: Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages. Despite various approaches to train such models, they have difficulty with zero-shot translation: translating between language pairs that were not together seen during training. In this paper we first diagnose why state-of-the-art multilingual NMT models that rely purely on parameter sharing, fail to generalize to unseen language pairs. We then propose auxiliary losses on the NMT encoder that impose representational invariance across languages. Our simple approach vastly improves zero-shot translation quality without regressing on supervised directions. For the first time, on WMT14 English-FrenchGerman, we achieve zero-shot performance that is on par with pivoting. We also demonstrate the easy scalability of our approach to multiple languages on the IWSLT 2017 shared task.

Includes members:
  Member "lingvo a modular and scalable framework for sequence to sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "universal neural machine translation for extremely low resource languages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "triangular architecture for rare language translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving back translation with uncertainty based confidence estimation")
  Member "a neural interlingua for multilingual machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "contextual parameter generation for universal neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "zero shot dual machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "unsupervised neural machine translation with weight sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robust unsupervised neural machine translation with adversarial training")

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)