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: "multi domain neural machine translation"
  Abstract: We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use multilingual NMT methods to create multi-domain translation systems, we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of pre-specified text domains is necessary, turns out that it is after all, but also that when it is not known quite high translation quality can be reached.

Connected through hyperedge HE-1 (induced by source paper "grammatical error correction and style transfer via zero shot monolingual translation"):
  with members:
    - "neural grammatical error correction with finite state transducers"
    - "multi task neural models for translating between styles within and across languages"
    - "unsupervised text style transfer using language models as discriminators"
    - "style transfer as unsupervised machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "disentangled representation learning for non parallel text style transfer"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "style transfer through back translation"

Connected through hyperedge HE-2 (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"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "multi domain neural machine translation with word level adaptive layer wise domain mixing"):
  with members:
    - "word based domain adaptation for neural machine translation"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "domain specialization a post training domain adaptation for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and 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)