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 "calibration of deep probabilistic models with decoupled bayesian neural networks"):
  Abstract: Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy performance in many tasks. However, recent works have pointed out that the outputs provided by these models are not well-calibrated, seriously limiting their use in critical decision scenarios. In this work, we propose to use a decoupled Bayesian stage, implemented with a Bayesian Neural Network (BNN), to map the uncalibrated probabilities provided by a DNN to calibrated ones, consistently improving calibration. Our results evidence that incorporating uncertainty provides more reliable probabilistic models, a critical condition for achieving good calibration. We report a generous collection of experimental results using high-accuracy DNNs in standardized image classification benchmarks, showing the good performance, flexibility and robust behavior of our approach with respect to several state-of-the-art calibration methods. Code for reproducibility is provided.

Includes members:
  Member "dirichlet based gaussian processes for large scale calibrated classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "learning for single shot confidence calibration in deep neural networks through stochastic inferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity")
  Member "ergodic measure preserving flows":
    also connected through other hyperedges:
      (none)
  Member "accurate uncertainties for deep learning using calibrated regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a financial service chatbot based on deep bidirectional transformers")
  Member "inference suboptimality in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "fixing variational bayes deterministic variational inference for bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a simple baseline for bayesian uncertainty in deep learning")

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)