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 "conditional response generation using variational alignment"):
  Abstract: Generating relevant/conditioned responses in dialog is challenging, and requires not only proper modelling of context in the conversation, but also the ability to generate fluent sentences during inference. In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. Our model first learns meaningful representations of sentences, and then uses a generator to \textit{match} the query with the response distribution. Latent codes from the latter are then used to generate responses. Both quantitative and qualitative evaluations show that our model generates more fluent, relevant and diverse responses than the existing state-of-the-art methods.

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 "probabilistic natural language generation with wasserstein autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "natural language generation with neural variational models")
  Member "a hierarchical latent structure for variational conversation modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "improving variational encoder decoders in dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network")
  Member "style transfer from non parallel text by cross alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "dailydialog a manually labelled multi turn dialogue dataset":
    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 "adversarial learning for neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non autoregressive neural dialogue generation")
  Member "variational attention for sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards generating long and coherent text with multi level latent variable models")

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)