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 "unsupervised pretraining encourages moderate sparseness"):
  Abstract: Abstract It is well known that direct training of deep neu-ralnetworkswillgenerallyleadtopoorresults. Amajor progress in recent years is the invention ofvarious pretraining methods to initialize networkparameters and it was shown that such method-s lead to good prediction performance. Howev-er, the reason for the success of pretraining hasnot been fully understood, although it was ar-gued that regularization and better optimizationplay certain roles. This paper provides anotherexplanation for the effectiveness of pretraining,where we show pretraining leads to a sparsenessof hidden unit activation in the resulting neuralnetworks. The main reason is that the pretrain-ing models can be interpreted as an adaptive s-parse coding. Compared to deep neural networkwith sigmoid function, our experimental resultson MNIST and Birdsong further support this s-parseness observation. 1. Introduction Deep neural networks (DNNs) have found many success-ful applications in recent years. However, it is well-knownthat if one trains such networks with the standard back-propagation algorithm from randomly initialized parame-ters, one typically ends up with models that have poor pre-diction performance. A major progress in DNNs researchis the invention of pretraining techniques for deep learn-ing (Hinton et al., 2006; Hinton & Salakhutdinov, 2006;Bengio et al., 2006; Bengio, 2009; Bengio et al., 2012).The main strategy is to employ layer-wise unsupervisedlearning procedures to initialize the DNN model parame-ters. A number of such unsupervised training techniqueshave been proposed, such as restricted Boltzmann ma-

Includes members:
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "better mixing via deep representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling")
  Member "non negative matrix factorization with sparseness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling overlapping beliefs by structured matrix factorization")

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)