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: "style transfer as unsupervised machine translation"
  Abstract: Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content. One main challenge in learning a style transfer system is a lack of parallel data where the source sentence is in one style and the target sentence in another style. With this constraint, in this paper, we adapt unsupervised machine translation methods for the task of automatic style transfer. We first take advantage of style-preference information and word embedding similarity to produce pseudo-parallel data with a statistical machine translation (SMT) framework. Then the iterative back-translation approach is employed to jointly train two neural machine translation (NMT) based transfer systems. To control the noise generated during joint training, a style classifier is introduced to guarantee the accuracy of style transfer and penalize bad candidates in the generated pseudo data. Experiments on benchmark datasets show that our proposed method outperforms previous state-of-the-art models in terms of both accuracy of style transfer and quality of input-output correspondence.

Connected through hyperedge HE-1 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-2 (induced by source paper "controllable unsupervised text attribute transfer via editing entangled latent representation"):
  with members:
    - "language models with transformers"
    - "unsupervised text style transfer using language models as discriminators"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "sentiment transfer using seq2seq adversarial autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "style transfer for texts retrain report errors compare with rewrites"):
  with members:
    - "unsupervised paraphrasing without translation"
    - "evaluating prose style transfer with the bible"
    - "multiple attribute text style transfer"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

Connected through hyperedge HE-4 (induced by source paper "xl editor post editing sentences with xlnet"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "a dual reinforcement learning framework for unsupervised text style transfer"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "text repair model for neural machine translation"
    - "levenshtein transformer"
    - "text infilling"
    - "kermit generative insertion based modeling for sequences"

Connected through hyperedge HE-5 (induced by source paper "generalized data augmentation for low resource translation"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "choosing transfer languages for cross lingual learning"
    - "massively multilingual neural machine translation"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "universal neural machine translation for extremely low resource languages"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "multi source neural machine translation with data augmentation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "shaped shared private encoder decoder for text style adaptation"

Connected through hyperedge HE-7 (induced by source paper "decomposing textual information for style transfer"):
  with members:
    - "style transfer for texts to err is human but error margins matter"
    - "evaluating prose style transfer with the bible"
    - "multiple attribute text style transfer"
    - "what is wrong with style transfer for texts"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "generating lyrics with variational autoencoder and multi modal artist embeddings"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

Connected through hyperedge HE-8 (induced by source paper "automatic fact guided sentence modification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bilinear attention networks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "allennlp a deep semantic natural language processing platform"
    - "fever a large scale dataset for fact extraction and verification"

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)