human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generating text through adversarial training using skip thought vectors"):
  Abstract: In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

Includes members:
  Member "deep learning for sentiment analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an end to end visual audio attention network for emotion recognition in user generated videos")
  Member "neural text generation a practical guide":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clinically accurate chest x ray report generation")
  Member "adversarial generation of natural language":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel framework for selection of gans for an application")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "recent trends in deep learning based natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sqwa stochastic quantized weight averaging for improving the generalization capability of low precision deep neural networks")
  Member "long text generation via adversarial training with leaked information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust explanations for visual question answering")
  Member "text generation based on generative adversarial nets with latent variable":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structured knowledge distillation for dense prediction")
  Member "seqgan sequence generative adversarial nets with policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)