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 "sampling free epistemic uncertainty estimation using approximated variance propagation"):
  Abstract: We present a sampling-free approach for computing the epistemic uncertainty of a neural network. Epistemic uncertainty is an important quantity for the deployment of deep neural networks in safety-critical applications, since it represents how much one can trust predictions on new data. Recently promising works were proposed using noise injection combined with Monte-Carlo sampling at inference time to estimate this quantity (e.g. Monte-Carlo dropout). Our main contribution is an approximation of the epistemic uncertainty estimated by these methods that does not require sampling, thus notably reducing the computational overhead. We apply our approach to large-scale visual tasks (i.e., semantic segmentation and depth regression) to demonstrate the advantages of our method compared to sampling-based approaches in terms of quality of the uncertainty estimates as well as of computational overhead.

Includes members:
  Member "inhibited softmax for uncertainty estimation in neural networks":
    also connected through other hyperedges:
      (none)
  Member "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "sampling free uncertainty estimation in gated recurrent units with exponential families":
    also connected through other hyperedges:
      (none)
  Member "efficient uncertainty estimation for semantic segmentation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")
  Member "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "noisy natural gradient as variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "long term on board prediction of people in traffic scenes under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic multiscale graph neural networks for 3d skeleton based human motion prediction")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")

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)