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: "multiple attribute text style transfer"
  Abstract: The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style". In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations. We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation. Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space. Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.

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"
    - "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 "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"
    - "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"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "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"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

Connected through hyperedge HE-5 (induced by source paper "unsupervised question answering by cloze translation"):
  with members:
    - "evaluating rewards for question generation models"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "efficient and robust question answering from minimal context over documents"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "what makes reading comprehension questions easier"
    - "identifying well formed natural language questions"

Connected through hyperedge HE-6 (induced by source paper "adapting language models for non parallel author stylized rewriting"):
  with members:
    - "a lexical syntactic and semantic perspective for understanding style in text"
    - "mass masked sequence to sequence pre training for language generation"
    - "evaluating style transfer for text"
    - "cross lingual language model pretraining"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "polite dialogue generation without parallel data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "educating text autoencoders latent representation guidance via denoising"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "universal transformers"
    - "eval all trust a few do wrong to none comparing sentence generation models"
    - "content preserving text generation with attribute controls"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-8 (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"
    - "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"
    - "guess who multilingual approach for the automated generation of author stylized poetry"

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)