human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "predicting distributions with linearizing belief networks"
  Abstract: Conditional belief networks introduce stochastic binary variables in neural networks. Contrary to a classical neural network, a belief network can predict more than the expected value of the output $Y$ given the input $X$. It can predict a distribution of outputs $Y$ which is useful when an input can admit multiple outputs whose average is not necessarily a valid answer. Such networks are particularly relevant to inverse problems such as image prediction for denoising, or text to speech. However, traditional sigmoid belief networks are hard to train and are not suited to continuous problems. This work introduces a new family of networks called linearizing belief nets or LBNs. A LBN decomposes into a deep linear network where each linear unit can be turned on or off by non-deterministic binary latent units. It is a universal approximator of real-valued conditional distributions and can be trained using gradient descent. Moreover, the linear pathways efficiently propagate continuous information and they act as multiplicative skip-connections that help optimization by removing gradient diffusion. This yields a model which trains efficiently and improves the state-of-the-art on image denoising and facial expression generation with the Toronto faces dataset.

Connected through hyperedge HE-1 (induced by source paper "left ventricle segmentation by modelling uncertainty in prediction of deep convolutional neural networks and adaptive thresholding inference"):
  with members:
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "visualizing the loss landscape of neural nets"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "densely connected convolutional networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "layer wise learning of stochastic neural networks with information bottleneck"):
  with members:
    - "opening the black box of deep neural networks via information"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "deep variational information bottleneck"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "stochasticnet forming deep neural networks via stochastic connectivity"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "language modeling with gated convolutional networks"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "factorization tricks for lstm networks"
    - "conditional image generation with pixelcnn decoders"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "exploring the limits of language modeling"
    - "pixel recurrent neural networks"
    - "deep residual learning for image recognition"
    - "strategies for training large vocabulary neural language models"

Connected through hyperedge HE-4 (induced by source paper "variational inference for monte carlo objectives"):
  with members:
    - "importance weighted autoencoders"
    - "muprop unbiased backpropagation for stochastic neural networks"
    - "draw a recurrent neural network for image generation"
    - "reinforcement learning neural turing machines revised"
    - "learning wake sleep recurrent attention models"
    - "adam a method for stochastic optimization"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"

Connected through hyperedge HE-5 (induced by source paper "improving variational auto encoders using householder flow"):
  with members:
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "language modeling with gated convolutional networks"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"

Question: Please tell me which class the center node 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)