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: "domain specialization a post training domain adaptation for neural machine translation"
  Abstract: Domain adaptation is a key feature in Machine Translation. It generally encompasses terminology, domain and style adaptation, especially for human post-editing workflows in Computer Assisted Translation (CAT). With Neural Machine Translation (NMT), we introduce a new notion of domain adaptation that we call "specialization" and which is showing promising results both in the learning speed and in adaptation accuracy. In this paper, we propose to explore this approach under several perspectives.

Connected through hyperedge HE-1 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "machine translation of restaurant reviews new corpus for domain adaptation and robustness"):
  with members:
    - "tagged back translation"
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-3 (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"
    - "neural machine translation training in a multi domain scenario"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "revisiting simple domain adaptation methods in unsupervised neural machine translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "domain adaptive inference for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised neural machine translation"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"

Connected through hyperedge HE-6 (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"
    - "multi domain neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "neural machine translation training in a multi domain scenario"):
  with members:
    - "challenging language dependent segmentation for arabic an application to machine translation and part of speech tagging"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "dynamic data selection for neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "an arabic hebrew parallel corpus of ted talks"
    - "edinburgh neural machine translation systems for wmt 16"
    - "neural machine translation of rare words with subword units"

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)