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 "quantization based regularization for autoencoders"):
  Abstract: Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models are susceptible to the overfitting problem and the so-called posterior collapse phenomenon. In this paper, we introduce a quantization-based regularizer in the bottleneck stage of autoencoder models to learn meaningful latent representations. We combine both perspectives of Vector Quantized-Variational AutoEncoders (VQ-VAE) and classical denoising regularization methods of neural networks. We interpret quantizers as regularizers that constrain latent representations while fostering a similarity-preserving mapping at the encoder. Before quantization, we impose noise on the latent codes and use a Bayesian estimator to optimize the quantizer-based representation. The introduced bottleneck Bayesian estimator outputs the posterior mean of the centroids to the decoder, and thus, is performing soft quantization of the noisy latent codes. We show that our proposed regularization method results in improved latent representations for both supervised learning and clustering downstream tasks when compared to autoencoders using other bottleneck structures.

Includes members:
  Member "preventing posterior collapse with delta vaes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "understanding and improving interpolation in autoencoders via an adversarial regularizer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi class novelty detection using mix up technique")
  Member "clustering with deep learning taxonomy and new methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "fast decoding in sequence models using discrete latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention")
  Member "som vae interpretable discrete representation learning on time series":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "are disentangled representations helpful for abstract visual reasoning")
  Member "information theoretic analysis of generalization capability of learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "advances in variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "soft to hard vector quantization for end to end learning compressible representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to structure an image with few colors")

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)