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 "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.

Includes members:
  Member "evaluating model calibration in classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning")
  Member "cyclical stochastic gradient mcmc for bayesian deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "confidence propagation through cnns for guided sparse depth regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t forget the past recurrent depth estimation from monocular video")
  Member "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smoke single stage monocular 3d object detection via keypoint estimation")
  Member "lightweight probabilistic deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "ocnet object context network for scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "pointpillars fast encoders for object detection from point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review deep learning on 3d point clouds")
  Member "pyro deep universal probabilistic programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")

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)