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 "deep autoregressive networks"):
  Abstract: We introduce a deep, generative autoencoder capable of learning hierarchies of distributed representations from data. Successive deep stochastic hidden layers are equipped with autoregressive connections, which enable the model to be sampled from quickly and exactly via ancestral sampling. We derive an efficient approximate parameter estimation method based on the minimum description length (MDL) principle, which can be seen as maximising a variational lower bound on the log-likelihood, with a feedforward neural network implementing approximate inference. We demonstrate state-of-the-art generative performance on a number of classic data sets: several UCI data sets, MNIST and Atari 2600 games.

Includes members:
  Member "generalized denoising auto encoders as generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi source deep gaussian process kernel learning")
  Member "estimating or propagating gradients through stochastic neurons for conditional computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "attention over parameters for dialogue systems")
  Member "the arcade learning environment an evaluation platform for general agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "generating sequences with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "a generative process for sampling contractive auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from variational to deterministic autoencoders")
  Member "variational bayesian inference with stochastic search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards a kernel based physical interpretation of model uncertainty")
  Member "learning representations by maximizing compression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning based video coding a review and a case study")
  Member "emergence of complex like cells in a temporal product network with local receptive fields":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting spatial invariance with low rank local connectivity")

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)