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: "measuring calibration in deep learning"
  Abstract: The reliability of a machine learning model's confidence in its predictions is critical for highrisk applications. Calibration-the idea that a model's predicted probabilities of outcomes reflect true probabilities of those outcomes-formalizes this notion. While analyzing the calibration of deep neural networks, we've identified core problems with the way calibration is currently measured. We design the Thresholded Adaptive Calibration Error (TACE) metric to resolve these pathologies and show that it outperforms other metrics, especially in settings where predictions beyond the maximum prediction that is chosen as the output class matter. There are many cases where what a practitioner cares about is the calibration of a specific prediction, and so we introduce a dynamic programming based Prediction Specific Calibration Error (PSCE) that smoothly considers the calibration of nearby predictions to give an estimate of the calibration error of a specific 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"
    - "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"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"

Connected through hyperedge HE-2 (induced by source paper "temporal probability calibration"):
  with members:
    - "calibration of encoder decoder models for neural machine translation"
    - "when does label smoothing help"
    - "huggingface s transformers state of the art natural language processing"
    - "pytorch an imperative style high performance deep learning library"
    - "probability calibration trees"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on calibration of modern neural networks"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "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-4 (induced by source paper "on manifold adversarial data augmentation improves uncertainty calibration"):
  with members:
    - "towards neural networks that provably know when they don t know"
    - "large scale adversarial representation learning"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "human uncertainty makes classification more robust"
    - "disentangling adversarial robustness and generalization"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "analyzing the role of model uncertainty for electronic health records"):
  with members:
    - "bayesian layers a module for neural network uncertainty"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "scalable and accurate deep learning for electronic health records"
    - "on calibration of modern neural networks"
    - "bayesian recurrent neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "characterizing sources of uncertainty to proxy calibration and disambiguate annotator and data bias"):
  with members:
    - "when does label smoothing help"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "verified uncertainty calibration"):
  with members:
    - "evaluating model calibration in classification"
    - "using pre training can improve model robustness and uncertainty"
    - "calibrated model based deep reinforcement learning"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "posterior calibration and exploratory analysis for natural language processing models"
    - "a tutorial on conformal prediction"

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)