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: "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
  Abstract: The goal of sentiment-to-sentiment "translation" is to change the underlying sentiment of a sentence while keeping its content. The main challenge is the lack of parallel data. To solve this problem, we propose a cycled reinforcement learning method that enables training on unpaired data by collaboration between a neutralization module and an emotionalization module. We evaluate our approach on two review datasets, Yelp and Amazon. Experimental results show that our approach significantly outperforms the state-of-the-art systems. Especially, the proposed method substantially improves the content preservation performance. The BLEU score is improved from 1.64 to 22.46 and from 0.56 to 14.06 on the two datasets, respectively.

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"
    - "domain adaptive text style transfer"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "blank language models"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "restoring ancient text using deep learning a case study on greek epigraphy"
    - "mask predict parallel decoding of conditional masked language models"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-3 (induced by source paper "cross domain cascaded deep feature translation"):
  with members:
    - "transgaga geometry aware unsupervised image to image translation"
    - "logan unpaired shape transform in latent overcomplete space"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "neural best buddies sparse cross domain correspondence"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "improving shape deformation in unsupervised image to image translation"

Connected through hyperedge HE-4 (induced by source paper "domain adaptive text style transfer"):
  with members:
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "multiple attribute text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "how sequence to sequence models perceive language styles"):
  with members:
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "demystifying neural style transfer"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "style transfer in text exploration and evaluation"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-8 (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"
    - "style transfer as unsupervised machine translation"
    - "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"

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)