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: "adversarial ranking for language generation"
  Abstract: Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "a novel framework for selection of gans for an application"):
  with members:
    - "a review on generative adversarial networks algorithms theory and applications"
    - "the six fronts of the generative adversarial networks"
    - "generative adversarial networks a survey and taxonomy"
    - "stabilizing generative adversarial network training a survey"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "improving generalization and stability of generative adversarial networks"
    - "adversarial video generation on complex datasets"

Connected through hyperedge HE-2 (induced by source paper "on leveraging pretrained gans for limited data generation"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "open event extraction from online text using a generative adversarial network"
    - "analyzing and improving the image quality of stylegan"
    - "generative adversarial network in medical imaging a review"
    - "a bert based transfer learning approach for hate speech detection in online social media"
    - "image generation from small datasets via batch statistics adaptation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution"):
  with members:
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "deep exemplar based colorization"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "self adversarial learning with comparative discrimination for text generation"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "adversarial learning for neural dialogue generation"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "long text generation via adversarial training with leaked information"

Connected through hyperedge HE-6 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"

Connected through hyperedge HE-7 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "improving sequence to sequence learning via optimal transport"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "sequence generation with guider network"
    - "policy optimization as wasserstein gradient flows"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "improving gans using optimal transport"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "generating informative and diverse conversational responses via adversarial information maximization"

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)