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 "araml a stable adversarial training framework for text generation"):
  Abstract: Most of the existing generative adversarial networks (GAN) for text generation suffer from the instability of reinforcement learning training algorithms such as policy gradient, leading to unstable performance. To tackle this problem, we propose a novel framework called Adversarial Reward Augmented Maximum Likelihood (ARAML). During adversarial training, the discriminator assigns rewards to samples which are acquired from a stationary distribution near the data rather than the generator's distribution. The generator is optimized with maximum likelihood estimation augmented by the discriminator's rewards instead of policy gradient. Experiments show that our model can outperform state-of-the-art text GANs with a more stable training process.

Includes members:
  Member "incorporating discriminator in sentence generation a gibbs sampling method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bilingual gan a step towards parallel text generation")
  Member "on accurate evaluation of gans for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "towards diverse text generation with inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self adversarial learning with comparative discrimination for text generation")
  Member "texygen a benchmarking platform for text generation models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "cgmh constrained sentence generation by metropolis hastings sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "commongen a constrained text generation dataset towards generative commonsense reasoning")
  Member "language gans falling short":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "the price of debiasing automatic metrics in natural language evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a human like open domain chatbot")
  Member "emotional chatting machine emotional conversation generation with internal and external memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")

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)