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 "made masked autoencoder for distribution estimation"):
  Abstract: There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demonstrate that this approach is competitive with state-of-the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators.

Includes members:
  Member "nice non linear independent components estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks")
  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 "deep autoregressive networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting spatial invariance with low rank local connectivity")
  Member "sum product networks a new deep architecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "training deep energy based models with f divergence minimization")
  Member "adadelta an adaptive learning rate method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "theano new features and speed improvements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mnn a universal and efficient inference engine")
  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")

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)