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: "natural parameter networks a class of probabilistic neural networks"
  Abstract: Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "sampling free learning of bayesian quantized neural networks"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "lightweight probabilistic deep networks"
    - "probabilistic binary neural networks"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "tensor networks for dimensionality reduction and large scale optimization part 2 applications and future perspectives"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-2 (induced by source paper "training free uncertainty estimation for neural networks"):
  with members:
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "reducing uncertainty in undersampled mri reconstruction with active acquisition"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deeper depth prediction with fully convolutional residual networks"
    - "loss aware binarization of deep networks"

Connected through hyperedge HE-3 (induced by source paper "a general framework for uncertainty estimation in deep learning"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "where is the information in a deep neural network"
    - "lightweight probabilistic deep networks"
    - "deep drone racing learning agile flight in dynamic environments"
    - "the limits and potentials of deep learning for robotics"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"

Connected through hyperedge HE-4 (induced by source paper "sampling free epistemic uncertainty estimation using approximated variance propagation"):
  with members:
    - "inhibited softmax for uncertainty estimation in neural networks"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "sampling free uncertainty estimation in gated recurrent units with exponential families"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "noisy natural gradient as variational inference"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-5 (induced by source paper "lightweight probabilistic deep networks"):
  with members:
    - "on calibration of modern neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "learning 3d object categories by looking around them"
    - "universal adversarial perturbations"
    - "towards open set deep networks"
    - "bayesian dark knowledge"
    - "robust convolutional neural networks under adversarial noise"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "sampling free uncertainty estimation in gated recurrent units with exponential families"):
  with members:
    - "lstm a search space odyssey"
    - "bayesian recurrent neural networks"
    - "contextual rnn gans for abstract reasoning diagram generation"
    - "video ladder networks"
    - "exploring the limits of language modeling"
    - "video pixel networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-7 (induced by source paper "inhibited softmax for uncertainty estimation in neural networks"):
  with members:
    - "adversarial distillation of bayesian neural network posteriors"
    - "deep bayesian active semi supervised learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "noisy natural gradient as variational inference"
    - "robust physical world attacks on machine learning models"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

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)