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 "fast predictive uncertainty for classification with bayesian deep networks"):
  Abstract: In Bayesian Deep Learning, distributions over the output of classification neural networks are approximated by first constructing a Gaussian distribution over the weights, then sampling from it to receive a distribution over the categorical output distribution. This is costly. We reconsider old work to construct a Dirichlet approximation of this output distribution, which yields an analytic map between Gaussian distributions in logit space and Dirichlet distributions (the conjugate prior to the categorical) in the output space. We argue that the resulting Dirichlet distribution has theoretical and practical advantages, in particular more efficient computation of the uncertainty estimate, scaling to large datasets and networks like ImageNet and DenseNet. We demonstrate the use of this Dirichlet approximation by using it to construct a lightweight uncertainty-aware output ranking for the ImageNet setup.

Includes members:
  Member "backpack packing more into backprop":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lookahead a far sighted alternative of magnitude based pruning")
  Member "deep learning for classical japanese literature":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improve sgd training via aligning min batches")
  Member "evaluating uncertainty quantification in end to end autonomous driving control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep neural network perception models and robust autonomous driving systems")
  Member "evidential deep learning to quantify classification uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "a baseline for detecting misclassified and out of distribution examples in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "optimizing neural networks with kronecker factored approximate curvature":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling adaptive gradient methods from learning rates")

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)