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: "evaluating scalable bayesian deep learning methods for robust computer vision"
  Abstract: While Deep Neural Networks (DNNs) have become the go-to approach in computer vision, the vast majority of these models fail to properly capture the uncertainty inherent in their predictions. Estimating this predictive uncertainty can be crucial, e.g. in automotive applications. In Bayesian deep learning, predictive uncertainty is often decomposed into the distinct types of aleatoric and epistemic uncertainty. The former can be estimated by letting a DNN output the parameters of a certain probability distribution. Epistemic uncertainty estimation is a more challenging problem, and while different scalable methods recently have emerged, no extensive comparison has been performed in a real-world setting. We therefore accept this task and propose a comprehensive evaluation framework for scalable epistemic uncertainty estimation methods. Our proposed framework is specifically designed to test the robustness required in real-world computer vision applications. We also apply this framework to provide the first properly conclusive comparison of the two current state-of-the-art scalable methods: ensembling and MC-dropout. Our comparison demonstrates that ensembling consistently provides more reliable and practically useful uncertainty estimates. Code is available at https://github.com/fregu856/evaluating_bdl.

Connected through hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "large scale structure of neural network loss landscapes"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-2 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong learning"):
  with members:
    - "analyzing the role of model uncertainty for electronic health records"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "experience replay for continual learning"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"
    - "on calibration of modern neural networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "dibs diversity inducing information bottleneck in model ensembles"):
  with members:
    - "mim mutual information machine"
    - "variational adversarial active learning"
    - "learning not to learn training deep neural networks with biased data"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "attention based ensemble for deep metric learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "dilated residual networks"

Connected through hyperedge HE-4 (induced by source paper "the case for bayesian deep learning"):
  with members:
    - "output constrained bayesian neural networks"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "functional variational bayesian neural networks"
    - "understanding generalization through visualizations"
    - "subspace inference for bayesian deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "the functional neural process"
    - "classifier agnostic saliency map extraction"

Connected through hyperedge HE-5 (induced by source paper "evaluating scalable uncertainty estimation methods for dnn based molecular property prediction"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "evaluating and calibrating uncertainty prediction in regression tasks"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "on calibration of modern neural networks"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"

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)