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: "seqgan sequence generative adversarial nets with policy gradient"
  Abstract: As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "description based text classification with reinforcement learning"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence"
    - "docbert bert for document classification"
    - "question answering is a format when is it useful"
    - "entity relation extraction as multi turn question answering"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "coreference resolution as query based span prediction"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"

Connected through hyperedge HE-3 (induced by source paper "robust explanations for visual question answering"):
  with members:
    - "explanation vs attention a two player game to obtain attention for vqa"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "toward controlled generation of text"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "the curious case of neural text degeneration"
    - "bertscore evaluating text generation with bert"
    - "on the use of bert for neural machine translation"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "discofuse a large scale dataset for discourse based sentence fusion"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "generalization in generation a closer look at exposure bias"

Connected through hyperedge HE-6 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "on convergence and stability of gans"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective"):
  with members:
    - "deep adversarial social recommendation"
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "kgat knowledge graph attention network for recommendation"
    - "deep learning to address candidate generation and cold start challenges in recommender systems a research survey"
    - "generating reliable friends via adversarial training to improve social recommendation"
    - "cold start playlist recommendation with multitask learning"
    - "exs explainable search using local model agnostic interpretability"
    - "adversarial collaborative auto encoder for top n recommendation"

Connected through hyperedge HE-8 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "a study of reinforcement learning for neural machine translation"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improving variational encoder decoders in dialogue generation"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "latent variable dialogue models and their diversity"

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.LG(Machine Learning)