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 "fixing gaussian mixture vaes for interpretable text generation"):
  Abstract: Variational auto-encoder (VAE) with Gaussian priors is effective in text generation. To improve the controllability and interpretability, we propose to use Gaussian mixture distribution as the prior for VAE (GMVAE), since it includes an extra discrete latent variable in addition to the continuous one. Unfortunately, training GMVAE using standard variational approximation often leads to the mode-collapse problem. We theoretically analyze the root cause --- maximizing the evidence lower bound of GMVAE implicitly aggregates the means of multiple Gaussian priors. We propose Dispersed-GMVAE (DGMVAE), an improved model for text generation. It introduces two extra terms to alleviate mode-collapse and to induce a better structured latent space. Experimental results show that DGMVAE outperforms strong baselines in several language modeling and text generation benchmarks.

Includes members:
  Member "topic guided variational autoencoders for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words")
  Member "unsupervised discrete sentence representation learning for interpretable neural dialog generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "spherical latent spaces for stable variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "paraphrase generation with latent bag of words")
  Member "toward controlled generation of text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust explanations for visual question answering")
  Member "key value retrieval networks for task oriented dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "a hybrid convolutional variational autoencoder for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic decoding for discrete data in variational autoencoders")
  Member "deep unsupervised clustering with gaussian mixture variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "latent intention dialogue models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")

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.LG(Machine Learning)