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 "towards coherent and cohesive long form text generation"):
  Abstract: Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called negative-critical sequence training, which is proposed to eliminate the need of training a separate critic for estimating baseline. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline -- recurrent attention-based bidirectional MLE-trained neural language model.

Includes members:
  Member "dialogwae multimodal response generation with conditional wasserstein auto encoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot")
  Member "unsupervised text style transfer using language models as discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blank language models")
  Member "discourse aware neural rewards for coherent text generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non autoregressive neural dialogue generation")
  Member "learning to write with cooperative discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "neural approaches to conversational ai":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention over parameters for dialogue systems")
  Member "generating informative and diverse conversational responses via adversarial information maximization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "attngan fine grained text to image generation with attentional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object centric image generation from layouts")
  Member "adversarial learning for neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "non autoregressive neural 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)