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: "what is wrong with style transfer for texts"
  Abstract: A number of recent machine learning papers work with an automated style transfer for texts and, counter to intuition, demonstrate that there is no consensus formulation of this NLP task. Different researchers propose different algorithms, datasets and target metrics to address it. This short opinion paper aims to discuss possible formalization of this NLP task in anticipation of a further growing interest to it.

Connected through hyperedge HE-1 (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"
    - "style transfer as unsupervised machine translation"
    - "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-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"
    - "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-3 (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"
    - "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 "low level linguistic controls for style transfer and content preservation"):
  with members:
    - "controlling personality based stylistic variation with neural natural language generators"
    - "language style transfer from sentences with arbitrary unknown styles"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "controlling linguistic style aspects in neural language generation"
    - "style transfer in text exploration and evaluation"
    - "shakespearizing modern language using copy enriched sequence to sequence models"
    - "bag of tricks for efficient text classification"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-5 (induced by source paper "alter auxiliary text rewriting tool for natural language generation"):
  with members:
    - "plan write and revise an interactive system for open domain story generation"
    - "evaluating style transfer for text"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating sentences by editing prototypes"
    - "attention is all you need"
    - "adversarially regularized autoencoders"
    - "style transfer in text exploration and evaluation"

Connected through hyperedge HE-6 (induced by source paper "guess who multilingual approach for the automated generation of author stylized poetry"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "learning to generate reviews and discovering sentiment"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "flexible and creative chinese poetry generation using neural memory"
    - "style transfer from non parallel text by cross alignment"
    - "neural text generation a practical guide"
    - "controlling linguistic style aspects in neural language generation"

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)