human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
  Abstract: We propose a generic framework to calibrate accuracy and confidence of a prediction in deep neural networks through stochastic inferences. We interpret stochastic regularization using a Bayesian model, and analyze the relation between predictive uncertainty of networks and variance of the prediction scores obtained by stochastic inferences for a single example. Our empirical study shows that the accuracy and the score of a prediction are highly correlated with the variance of multiple stochastic inferences given by stochastic depth or dropout. Motivated by this observation, we design a novel variance-weighted confidence-integrated loss function that is composed of two cross-entropy loss terms with respect to ground-truth and uniform distribution, which are balanced by variance of stochastic prediction scores. The proposed loss function enables us to learn deep neural networks that predict confidence calibrated scores using a single inference. Our algorithm presents outstanding confidence calibration performance and improves classification accuracy when combined with two popular stochastic regularization techniques---stochastic depth and dropout---in multiple models and datasets; it alleviates overconfidence issue in deep neural networks significantly by training networks to achieve prediction accuracy proportional to confidence of prediction.

Connected through hyperedge HE-1 (induced by source paper "mix n match ensemble and compositional methods for uncertainty calibration in deep learning"):
  with members:
    - "verified uncertainty calibration"
    - "evaluating model calibration in classification"
    - "when does label smoothing help"
    - "measuring calibration in deep learning"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "non parametric calibration for classification"
    - "pytorch an imperative style high performance deep learning library"
    - "dirichlet based gaussian processes for large scale calibrated classification"

Connected through hyperedge HE-2 (induced by source paper "intra order preserving functions for calibration of multi class neural networks"):
  with members:
    - "unconstrained monotonic neural networks"
    - "when does label smoothing help"
    - "unsupervised temperature scaling post processing unsupervised calibration of deep models decisions"
    - "measuring calibration in deep learning"
    - "on calibration of modern neural networks"
    - "mixup beyond empirical risk minimization"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "regularizing neural networks by penalizing confident output distributions"

Connected through hyperedge HE-3 (induced by source paper "calibration of deep probabilistic models with decoupled bayesian neural networks"):
  with members:
    - "dirichlet based gaussian processes for large scale calibrated classification"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "ergodic measure preserving flows"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "inference suboptimality in variational autoencoders"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-4 (induced by source paper "evaluation of neural network uncertainty estimation with application to resource constrained platforms"):
  with members:
    - "towards increased trustworthiness of deep learning segmentation methods on cardiac mri"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "distance based confidence score for neural network classifiers"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "what do we need to build explainable ai systems for the medical domain"

Connected through hyperedge HE-5 (induced by source paper "characterizing sources of uncertainty to proxy calibration and disambiguate annotator and data bias"):
  with members:
    - "when does label smoothing help"
    - "measuring calibration in deep learning"
    - "on calibration of modern neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"

Question: Please tell me which class the center node 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)