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 "dropout strikes back improved uncertainty estimation via diversity sampled implicit ensembles"):
  Abstract: Modern machine learning models usually do not extrapolate well, i.e., they often have high prediction errors in the regions of sample space lying far from the training data. In high dimensional spaces detecting out-of-distribution points becomes a non-trivial problem. Thus, uncertainty estimation for model predictions becomes crucial for the successful application of machine learning models in many applications. In this work, we show that increasing the diversity of realizations sampled from a neural network with dropout helps to improve the quality of uncertainty estimation. In a series of experiments on simulated and real-world data, we demonstrate that diversification via determinantal point processes-based sampling allows achieving state-of-the-art results in uncertainty estimation for regression and classification tasks. Importantly, our approach does not require any modification to the models or training procedures, allowing for straightforward application to any deep learning model with dropout layers.

Includes members:
  Member "maximizing overall diversity for improved uncertainty estimates in deep ensembles":
    also connected through other hyperedges:
      (none)
  Member "subspace inference for bayesian deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "out of distribution detection using an ensemble of self supervised leave out classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "dropout based active learning for regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constraining the parameters of high dimensional models with active learning")
  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 "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 "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action for better prediction")

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)