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: "confidence scoring using whitebox meta models with linear classifier probes"
  Abstract: We propose a confidence scoring mechanism for multi-layer neural networks based on a paradigm of a base model and a meta-model. The confidence score is learned by the meta-model using features derived from the base model -- a deep multi-layer neural network -- considered a whitebox. As features, we investigate linear classifier probes inserted between the various layers of the base model and trained using each layer's intermediate activations. Experiments show that this approach outperforms various baselines in a filtering task, i.e., task of rejecting samples with low confidence. Experimental results are presented using CIFAR-10 and CIFAR-100 dataset with and without added noise exploring various aspects of the method.

Connected through hyperedge HE-1 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "a less biased evaluation of out of distribution sample detectors"

Connected through hyperedge HE-2 (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"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "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-3 (induced by source paper "accurate layerwise interpretable competence estimation"):
  with members:
    - "evaluation of neural network uncertainty estimation with application to resource constrained platforms"
    - "classification uncertainty of deep neural networks based on gradient information"
    - "yolov3 an incremental improvement"
    - "on calibration of modern neural networks"
    - "prototypical networks for few shot learning"
    - "confidence estimation in deep neural networks via density modelling"
    - "distance based confidence score for neural network classifiers"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

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"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "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"

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)