human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "explaining and generalizing back translation through wake sleep"
  Abstract: Back-translation has become a commonly employed heuristic for semi-supervised neural machine translation. The technique is both straightforward to apply and has led to state-of-the-art results. In this work, we offer a principled interpretation of back-translation as approximate inference in a generative model of bitext and show how the standard implementation of back-translation corresponds to a single iteration of the wake-sleep algorithm in our proposed model. Moreover, this interpretation suggests a natural iterative generalization, which we demonstrate leads to further improvement of up to 1.6 BLEU.

Connected through hyperedge HE-1 (induced by source paper "tagged back translation"):
  with members:
    - "text repair model for neural machine translation"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "a call for clarity in reporting bleu scores"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"
    - "understanding back translation at scale"

Connected through hyperedge HE-2 (induced by source paper "iterative batch back translation for neural machine translation a conceptual model"):
  with members:
    - "tagged back translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "generalizing back translation in neural machine translation"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "analyzing uncertainty in neural machine translation"
    - "understanding back translation at scale"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "regularizing neural networks by penalizing confident output distributions"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (induced by source paper "text repair model for neural machine translation"):
  with members:
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"
    - "the university of cambridge s machine translation systems for wmt18"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "a call for clarity in reporting bleu scores"
    - "phrase based neural unsupervised machine translation"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "edinburgh neural machine translation systems for wmt 16"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-5 (induced by source paper "auto encoding variational neural machine translation"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "spherical latent spaces for stable variational autoencoders"
    - "a call for clarity in reporting bleu scores"
    - "joint training for neural machine translation models with monolingual data"
    - "generative neural machine translation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "fixing a broken elbo"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-6 (induced by source paper "understanding back translation at scale"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "universal neural machine translation for extremely low resource languages"
    - "scaling neural machine translation"
    - "hierarchical neural story generation"
    - "a call for clarity in reporting bleu scores"
    - "phrase based neural unsupervised machine translation"

Question: Please tell me which class the center node 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)