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: "structured content preservation for unsupervised text style transfer"
  Abstract: Text style transfer aims to modify the style of a sentence while keeping its content unchanged. Recent style transfer systems often fail to faithfully preserve the content after changing the style. This paper proposes a structured content preserving model that leverages linguistic information in the structured fine-grained supervisions to better preserve the style-independent content during style transfer. In particular, we achieve the goal by devising rich model objectives based on both the sentence's lexical information and a language model that conditions on content. The resulting model therefore is encouraged to retain the semantic meaning of the target sentences. We perform extensive experiments that compare our model to other existing approaches in the tasks of sentiment and political slant transfer. Our model achieves significant improvement in terms of both content preservation and style transfer in automatic and human evaluation.

Connected through hyperedge HE-1 (induced by source paper "imat unsupervised text attribute transfer via iterative matching and translation"):
  with members:
    - "style transfer as unsupervised machine translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "style transfer for texts retrain report errors compare with rewrites"):
  with members:
    - "unsupervised paraphrasing without translation"
    - "style transfer as unsupervised machine 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"

Connected through hyperedge HE-3 (induced by source paper "toward unsupervised text content manipulation"):
  with members:
    - "text infilling"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "decomposing textual information for style transfer"):
  with members:
    - "style transfer for texts to err is human but error margins matter"
    - "style transfer as unsupervised machine 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"

Connected through hyperedge HE-5 (induced by source paper "style transfer for texts to err is human but error margins matter"):
  with members:
    - "unsupervised paraphrasing without translation"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "guess who multilingual approach for the automated generation of author stylized poetry"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "adversarial decomposition of text representation"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "generating sentences by editing prototypes"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-6 (induced by source paper "formality style transfer with hybrid textual annotations"):
  with members:
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "convolutional sequence to sequence learning"
    - "improved neural text attribute transfer with non parallel data"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "improving neural machine translation models with monolingual data"

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)