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 adaptive text style transfer"
  Abstract: Text style transfer without parallel data has achieved some practical success. However, in the scenario where less data is available, these methods may yield poor performance. In this paper, we examine domain adaptation for text style transfer to leverage massively available data from other domains. These data may demonstrate domain shift, which impedes the benefits of utilizing such data for training. To address this challenge, we propose simple yet effective domain adaptive text style transfer models, enabling domain-adaptive information exchange. The proposed models presumably learn from the source domain to: (i) distinguish stylized information and generic content information; (ii) maximally preserve content information; and (iii) adaptively transfer the styles in a domain-aware manner. We evaluate the proposed models on two style transfer tasks (sentiment and formality) over multiple target domains where only limited non-parallel data is available. Extensive experiments demonstrate the effectiveness of the proposed model compared to the baselines.

Connected through hyperedge HE-1 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "zero shot fine grained style transfer leveraging distributed continuous style representations to transfer to unseen styles"
    - "disentangled representation learning for non parallel text style transfer"
    - "multiple attribute text style transfer"
    - "unsupervised stylish image description generation via domain layer norm"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "style example guided text generation using generative adversarial transformers"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "pretraining based natural language generation for text summarization"
    - "multi task deep neural networks for natural language understanding"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"

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)