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 "multimodal transitions for generative stochastic networks"):
  Abstract: Generative Stochastic Networks (GSNs) have been recently introduced as an alternative to traditional probabilistic modeling: instead of parametrizing the data distribution directly, one parametrizes a transition operator for a Markov chain whose stationary distribution is an estimator of the data generating distribution. The result of training is therefore a machine that generates samples through this Markov chain. However, the previously introduced GSN consistency theorems suggest that in order to capture a wide class of distributions, the transition operator in general should be multimodal, something that has not been done before this paper. We introduce for the first time multimodal transition distributions for GSNs, in particular using models in the NADE family (Neural Autoregressive Density Estimator) as output distributions of the transition operator. A NADE model is related to an RBM (and can thus model multimodal distributions) but its likelihood (and likelihood gradient) can be computed easily. The parameters of the NADE are obtained as a learned function of the previous state of the learned Markov chain. Experiments clearly illustrate the advantage of such multimodal transition distributions over unimodal GSNs.

Includes members:
  Member "bounding the test log likelihood of generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the quantitative analysis of decoder based generative models")
  Member "deep generative stochastic networks trainable by backprop":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders")
  Member "generalized denoising auto encoders as generative models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi source deep gaussian process kernel learning")
  Member "what regularized auto encoders learn from the data generating distribution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "better mixing via deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling")
  Member "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "music generation by deep learning challenges and directions")

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)