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 adaptation of neural machine translation by lexicon induction"
  Abstract: It has been previously noted that neural machine translation (NMT) is very sensitive to domain shift. In this paper, we argue that this is a dual effect of the highly lexicalized nature of NMT, resulting in failure for sentences with large numbers of unknown words, and lack of supervision for domain-specific words. To remedy this problem, we propose an unsupervised adaptation method which fine-tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus. Specifically, we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences. In five domains over twenty pairwise adaptation settings and two model architectures, our method achieves consistent improvements without using any in-domain parallel sentences, improving up to 14 BLEU over unadapted models, and up to 2 BLEU over strong back-translation baselines.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation for neural machine translation with iterative back translation"):
  with members:
    - "cross lingual language model pretraining"
    - "a survey of domain adaptation for neural machine translation"
    - "unsupervised neural machine translation"
    - "domain control for neural machine translation"
    - "fast domain adaptation for neural machine translation"
    - "unsupervised pretraining for sequence to sequence learning"
    - "improving neural machine translation models with monolingual data"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-2 (induced by source paper "domain differential adaptation for neural machine translation"):
  with members:
    - "compare mt a tool for holistic comparison of language generation systems"
    - "non parametric adaptation for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "table to text natural language generation with unseen schemas"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "domain adaptive dialog generation via meta learning"
    - "step by step separating planning from realization in neural data to text generation"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "natural language to structured query generation via meta learning"
    - "data to text generation with content selection and planning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-4 (induced by source paper "revisiting simple domain adaptation methods in unsupervised neural machine translation"):
  with members:
    - "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"
    - "dynamic data selection for neural 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)