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: "unsupervised text style transfer using language models as discriminators"
  Abstract: Binary classifiers are often employed as discriminators in GAN-based unsupervised style transfer systems to ensure that transferred sentences are similar to sentences in the target domain. One difficulty with this approach is that the error signal provided by the discriminator can be unstable and is sometimes insufficient to train the generator to produce fluent language. In this paper, we propose a new technique that uses a target domain language model as the discriminator, providing richer and more stable token-level feedback during the learning process. We train the generator to minimize the negative log likelihood (NLL) of generated sentences, evaluated by the language model. By using a continuous approximation of discrete sampling under the generator, our model can be trained using back-propagation in an end- to-end fashion. Moreover, our empirical results show that when using a language model as a structured discriminator, it is possible to forgo adversarial steps during training, making the process more stable. We compare our model with previous work using convolutional neural networks (CNNs) as discriminators and show that our approach leads to improved performance on three tasks: word substitution decipherment, sentiment modification, and related language translation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a probabilistic formulation of unsupervised text style transfer"):
  with members:
    - "an effective approach to unsupervised machine translation"
    - "evaluating style transfer for text"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "universal adversarial perturbations for speech recognition systems"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "adversarial initialization when your network performs the way i want"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-4 (induced by source paper "towards coherent and cohesive long form text generation"):
  with members:
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "discourse aware neural rewards for coherent text generation"
    - "learning to write with cooperative discriminators"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-5 (induced by source paper "controllable unsupervised text attribute transfer via editing entangled latent representation"):
  with members:
    - "language models with transformers"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "target guided open domain conversation"):
  with members:
    - "toward unsupervised text content manipulation"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "towards deep conversational recommendations"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "decoupling strategy and generation in negotiation dialogues"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "emergent communication through negotiation"

Connected through hyperedge HE-7 (induced by source paper "text modeling with syntax aware variational autoencoders"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "variational knowledge graph reasoning"
    - "syntax directed variational autoencoder for structured data"
    - "dirichlet variational autoencoder for text modeling"
    - "linguistically informed self attention for semantic role labeling"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-8 (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"
    - "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"
    - "shaped shared private encoder decoder for text style adaptation"

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)