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: "evaluating model calibration in classification"
  Abstract: Probabilistic classifiers output a probability distribution on target classes rather than just a class prediction. Besides providing a clear separation of prediction and decision making, the main advantage of probabilistic models is their ability to represent uncertainty about predictions. In safety-critical applications, it is pivotal for a model to possess an adequate sense of uncertainty, which for probabilistic classifiers translates into outputting probability distributions that are consistent with the empirical frequencies observed from realized outcomes. A classifier with such a property is called calibrated. In this work, we develop a general theoretical calibration evaluation framework grounded in probability theory, and point out subtleties present in model calibration evaluation that lead to refined interpretations of existing evaluation techniques. Lastly, we propose new ways to quantify and visualize miscalibration in probabilistic classification, including novel multidimensional reliability diagrams.

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

Connected through hyperedge HE-2 (induced by source paper "quantile regularization towards implicit calibration of regression models"):
  with members:
    - "verified uncertainty calibration"
    - "when does label smoothing help"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "mixup beyond empirical risk minimization"
    - "regularizing neural networks by penalizing confident output distributions"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "evaluating scalable bayesian deep learning methods for robust computer vision"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "lightweight probabilistic deep networks"
    - "ocnet object context network for scene parsing"
    - "pointpillars fast encoders for object detection from point clouds"
    - "pyro deep universal probabilistic programming"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

Connected through hyperedge HE-4 (induced by source paper "are graph neural networks miscalibrated"):
  with members:
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "fast graph representation learning with pytorch geometric"
    - "deep weighted averaging classifiers"
    - "pitfalls of graph neural network evaluation"
    - "graph networks as learnable physics engines for inference and control"
    - "a new loss function for temperature scaling to have better calibrated deep networks"
    - "relational inductive biases deep learning and graph networks"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-5 (induced by source paper "beyond temperature scaling obtaining well calibrated multiclass probabilities with dirichlet calibration"):
  with members:
    - "verified uncertainty calibration"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "dirichlet based gaussian processes for large scale calibrated classification"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "wide residual networks"

Connected through hyperedge HE-6 (induced by source paper "verified uncertainty calibration"):
  with members:
    - "measuring calibration in deep learning"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "calibration with bias corrected temperature scaling improves domain adaptation under label shift in modern neural networks"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "what is the effect of importance weighting in deep learning"
    - "on calibration of modern neural networks"
    - "reliable confidence estimation via online learning"
    - "on causal and anticausal learning"

Connected through hyperedge HE-8 (induced by source paper "em with bias corrected calibration is hard to beat at label shift adaptation"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"
    - "reliable confidence estimation via online learning"
    - "on causal and anticausal learning"

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)