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 "a discrete cvae for response generation on short text conversation"):
  Abstract: Neural conversation models such as encoder-decoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoder(CVAE) which maximizes the lower bound on the conditional log-likelihood on a continuous latent variable. With different sampled la-tent variables, the model is expected to generate diverse responses. Although the CVAE-based models have shown tremendous potential, their improvement of generating high-quality responses is still unsatisfactory. In this paper, we introduce a discrete latent variable with an explicit semantic meaning to improve the CVAE on short-text conversation. A major advantage of our model is that we can exploit the semantic distance between the latent variables to maintain good diversity between the sampled latent variables. Accordingly, we pro-pose a two-stage sampling approach to enable efficient diverse variable selection from a large latent space assumed in the short-text conversation task. Experimental results indicate that our model outperforms various kinds of generation models under both automatic and human evaluations and generates more diverse and in-formative responses.

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 "generating multiple diverse responses for short text conversation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "unsupervised discrete sentence representation learning for interpretable neural dialog generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "another diversity promoting objective function for neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dlgnet a transformer based model for dialogue response generation")
  Member "generating informative and diverse conversational responses via adversarial information maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "latent intention dialogue models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")
  Member "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  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)