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 "evaluation of neural network uncertainty estimation with application to resource constrained platforms"):
  Abstract: The ability to accurately estimate uncertainties in neural network predictions is of great importance in many critical tasks. In this paper, we first analyze the intrinsic relation between two main use cases of uncertainty estimation, i.e., selective prediction and confidence calibration. We then reveal the potential issues with the existing quality metrics for uncertainty estimation and propose new metrics to mitigate them. Finally, we apply these new metrics to resource-constrained platforms such as autonomous driver assistance systems where the quality of uncertainty estimation is critical. By exploring the trade-off between the model size and the estimation quality, a missing piece in the literature, some interesting trends are observed.

Includes members:
  Member "towards increased trustworthiness of deep learning segmentation methods on cardiac mri":
    also connected through other hyperedges:
      (none)
  Member "leveraging uncertainty estimates for predicting segmentation quality":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity")
  Member "learning for single shot confidence calibration in deep neural networks through stochastic inferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning")
  Member "confidence scoring using whitebox meta models with linear classifier probes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity")
  Member "enhancing the reliability of out of distribution image detection in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "on calibration of modern neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "distance based confidence score for neural network classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)