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 "rate distortion optimization guided autoencoder for generative approach with quantitatively measurable latent space"):
  Abstract: In the generative model approach of machine learning, it is essential to acquire an accurate probabilistic model and compress the dimension of data for easy treatment. However, in the conventional deep-autoencoder based generative model such as VAE, the probability of the real space cannot be obtained correctly from that of in the latent space, because the scaling between both spaces is not controlled. This has also been an obstacle to quantifying the impact of the variation of latent variables on data. In this paper, we propose Rate-Distortion Optimization guided autoencoder, in which the Jacobi matrix from real space to latent space has orthonormality. It is proved theoretically and experimentally that (i) the probability distribution of the latent space obtained by this model is proportional to the probability distribution of the real space because Jacobian between two spaces is constant; (ii) our model behaves as non-linear PCA, where energy of acquired latent space is concentrated on several principal components and the influence of each component can be evaluated quantitatively. Furthermore, to verify the usefulness on the practical application, we evaluate its performance in unsupervised anomaly detection and it outperforms current state-of-the-art methods.

Includes members:
  Member "exact rate distortion in autoencoders via echo noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a free energy principle for representation learning")
  Member "adversarially learned anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "memory augmented generative adversarial networks for anomaly detection")
  Member "recent advances in autoencoder based representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "variational autoencoders pursue pca directions by accident":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on implicit regularization in beta vaes")
  Member "neural discrete representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "variational deep embedding an unsupervised and generative approach to clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "density modeling of images using a generalized normalization transformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)