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: "neural machine translation training in a multi domain scenario"
  Abstract: In this paper, we explore alternative ways to train a neural machine translation system in a multi-domain scenario. We investigate data concatenation (with fine tuning), model stacking (multi-level fine tuning), data selection and weighted ensemble. We evaluate these methods based on three criteria: i) translation quality, ii) training time, and iii) robustness towards out-of-domain tests. Our findings on Arabic-English and German-English language pairs show that the best translation quality can be achieved by building an initial system on a concatenation of available out-of-domain data and then fine-tuning it on in-domain data. Model stacking works best when training begins with the furthest out-of-domain data and the model is incrementally fine-tuned with the next furthest domain and so on. Data selection did not give the best results, but can be considered as a decent compromise between training time and translation quality. A weighted ensemble of different individual models performed better than data selection. It is beneficial in a scenario when there is no time for fine-tuning.

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"
    - "explaining and generalizing back translation through wake sleep"
    - "phrase based neural unsupervised machine translation"
    - "bi directional neural machine translation with synthetic parallel data"

Connected through hyperedge HE-2 (induced by source paper "learning a multitask curriculum for neural machine translation"):
  with members:
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "multilingual multi domain adaptation approaches for neural machine translation"):
  with members:
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "how transferable are neural networks in nlp applications"

Connected through hyperedge HE-5 (induced by source paper "iterative dual domain adaptation for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "non parametric adaptation for neural machine translation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "semantic neural machine translation using amr"
    - "a survey of domain adaptation for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "multi domain neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "domain adaptive inference for neural machine translation"):
  with members:
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "attention is all you need"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "regularization techniques for fine tuning in neural machine translation"
    - "six challenges for neural machine translation"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning based curriculum optimization for neural machine translation"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "reinforced co training"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "automated curriculum learning for neural networks"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "fast domain adaptation for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "a survey of domain adaptation for neural machine translation"):
  with members:
    - "domain generalization via conditional invariant representation"
    - "multi adversarial domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "attention is all you need"
    - "neural machine translation and sequence to sequence models a tutorial"
    - "building a neural machine translation system using only synthetic parallel data"
    - "adversarial discriminative domain adaptation"
    - "multiple source domain adaptation with adversarial training of neural networks"

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)