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: "a dual reinforcement learning framework for unsupervised text style transfer"
  Abstract: Unsupervised text style transfer aims to transfer the underlying style of text but keep its main content unchanged without parallel data. Most existing methods typically follow two steps: first separating the content from the original style, and then fusing the content with the desired style. However, the separation in the first step is challenging because the content and style interact in subtle ways in natural language. Therefore, in this paper, we propose a dual reinforcement learning framework to directly transfer the style of the text via a one-step mapping model, without any separation of content and style. Specifically, we consider the learning of the source-to-target and target-to-source mappings as a dual task, and two rewards are designed based on such a dual structure to reflect the style accuracy and content preservation, respectively. In this way, the two one-step mapping models can be trained via reinforcement learning, without any use of parallel data. Automatic evaluations show that our model outperforms the state-of-the-art systems by a large margin, especially with more than 8 BLEU points improvement averaged on two benchmark datasets. Human evaluations also validate the effectiveness of our model in terms of style accuracy, content preservation and fluency. Our code and data, including outputs of all baselines and our model are available at this https URL.

Connected through hyperedge HE-1 (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"
    - "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"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "rethinking text attribute transfer a lexical analysis"):
  with members:
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"

Connected through hyperedge HE-3 (induced by source paper "a hierarchical reinforced sequence operation method for unsupervised text style transfer"):
  with members:
    - "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"
    - "an operation sequence model for explainable neural machine translation"
    - "reinforcement learning for relation classification from noisy data"
    - "a hierarchical framework for relation extraction with reinforcement learning"
    - "toward controlled generation of text"

Connected through hyperedge HE-4 (induced by source paper "a neural approach to irony generation"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "content preserving text generation with attribute controls"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "attention is all you need"
    - "style transfer in text exploration and evaluation"
    - "improving neural machine translation models with monolingual data"
    - "adam a method for stochastic optimization"

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)