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: "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
  Abstract: We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus function in the final layer, we estimate aleatoric and epistemic uncertainty in a coherent manner. The intractable posterior probability distributions over weights are inferred by Bayes by Backprop. Firstly, we demonstrate how this reliable variational inference method can serve as a fundamental construct for various network architectures. On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally. Secondly, we examine how our proposed measure for aleatoric and epistemic uncertainties is derived and validate it on the aforementioned datasets.

Connected through hyperedge HE-1 (induced by source paper "uncertainty guided continual learning with bayesian neural networks"):
  with members:
    - "large scale incremental learning"
    - "facilitating bayesian continual learning by natural gradients and stein gradients"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "task free continual learning"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "efficient lifelong learning with a gem"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "end to end incremental learning"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive guide to bayesian convolutional neural network with variational inference"):
  with members:
    - "exploring sparsity in recurrent neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "bayesian recurrent neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "curiosity driven exploration in deep reinforcement learning via bayesian neural networks"
    - "stochastic backpropagation through mixture density distributions"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"

Connected through hyperedge HE-3 (induced by source paper "bayesian layers a module for neural network uncertainty"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "image transformer"
    - "learning latent dynamics for planning from pixels"
    - "pyro deep universal probabilistic programming"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "mesh tensorflow deep learning for supercomputers"
    - "in datacenter performance analysis of a tensor processing unit"
    - "the reversible residual network backpropagation without storing activations"

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)