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 "efficient evaluation time uncertainty estimation by improved distillation"):
  Abstract: In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks. For this, we propose a modified knowledge distillation procedure that achieves state-of-the-art uncertainty estimates both for in and out-of-distribution samples. Our contributions include a) demonstrating and adapting to distillation's regularization effect b) proposing a novel target teacher distribution c) a simple augmentation procedure to improve out-of-distribution uncertainty estimates d) shedding light on the distillation procedure through comprehensive set of experiments.

Includes members:
  Member "reducing overconfident errors outside the known distribution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "angular visual hardness")
  Member "discrimination aware channel pruning for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "large scale distributed neural network training through online distillation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "dropout distillation for efficiently estimating model confidence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving generalization and robustness with noisy collaboration in knowledge distillation")
  Member "fixing variational bayes deterministic variational inference for bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "snapshot ensembles train 1 get m for free":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "selective classification for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "utilizing network properties to detect erroneous inputs")

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)