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: "improved neural text attribute transfer with non parallel data"
  Abstract: Text attribute transfer using non-parallel data requires methods that can perform disentanglement of content and linguistic attributes. In this work, we propose multiple improvements over the existing approaches that enable the encoder-decoder framework to cope with the text attribute transfer from non-parallel data. We perform experiments on the sentiment transfer task using two datasets. For both datasets, our proposed method outperforms a strong baseline in two of the three employed evaluation metrics.

Connected through hyperedge HE-1 (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"
    - "multiple attribute text style transfer"
    - "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"

Connected through hyperedge HE-2 (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"
    - "what is wrong with style transfer for texts"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "revision in continuous space unsupervised text style transfer without adversarial learning"):
  with members:
    - "mu forcing training variational recurrent autoencoders for text generation"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "bfgan backward and forward generative adversarial networks for lexically constrained sentence generation"
    - "neural architecture optimization"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-4 (induced by source paper "formality style transfer with hybrid textual annotations"):
  with members:
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "structured content preservation for unsupervised text style transfer"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "convolutional sequence to sequence learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "improving neural machine translation models with monolingual data"

Connected through hyperedge HE-5 (induced by source paper "unsupervised evaluation metrics and learning criteria for non parallel textual transfer"):
  with members:
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "approximate ranking from pairwise comparisons"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "fighting offensive language on social media with unsupervised text style transfer"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"

Connected through hyperedge HE-6 (induced by source paper "unsupervised stylish image description generation via domain layer norm"):
  with members:
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "style transfer through back translation"
    - "style transfer from non parallel text by cross alignment"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unsupervised image to image translation networks"
    - "contrastive learning for image captioning"
    - "style transfer in text exploration and evaluation"

Connected through hyperedge HE-7 (induced by source paper "delete retrieve generate a simple approach to sentiment and style transfer"):
  with members:
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"
    - "unsupervised machine translation using monolingual corpora only"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "adversarially regularized autoencoders"
    - "unsupervised neural machine translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "style transfer in text exploration and evaluation"

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)