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: "obtaining calibrated probabilities from boosting"
  Abstract: Boosted decision trees typically yield good accuracy, precision, and ROC area. However, because the outputs from boosting are not well calibrated posterior probabilities, boosting yields poor squared error and cross-entropy. We empirically demonstrate why AdaBoost predicts distorted probabilities and examine three calibration methods for correcting this distortion: Platt Scaling, Isotonic Regression, and Logistic Correction. We also experiment with boosting using log-loss instead of the usual exponential loss. Experiments show that Logistic Correction and boosting with log-loss work well when boosting weak models such as decision stumps, but yield poor performance when boosting more complex models such as full decision trees. Platt Scaling and Isotonic Regression, however, significantly improve the probabilities predicted by

Connected through hyperedge HE-1 (induced by source paper "better boosting with bandits for online learning"):
  with members:
    - "online isotonic regression"
    - "thompson sampling in switching environments with bayesian online change point detection"
    - "further optimal regret bounds for thompson sampling"
    - "unbiased offline evaluation of contextual bandit based news article recommendation algorithms"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-2 (induced by source paper "better classifier calibration for small data sets"):
  with members:
    - "difference target propagation"

Connected through hyperedge HE-3 (induced by source paper "non parametric calibration for classification"):
  with members:
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "rotation equivariant cnns for digital pathology"
    - "on calibration of modern neural networks"
    - "on fairness and calibration"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "advances in variational inference"

Connected through hyperedge HE-4 (induced by source paper "ccmi classifier based conditional mutual information estimation"):
  with members:
    - "estimators for multivariate information measures in general probability spaces"
    - "on calibration of modern neural networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"
    - "demystifying fixed k nearest neighbor information estimators"
    - "conditional generative adversarial nets"
    - "efficient estimation of mutual information for strongly dependent variables"
    - "learning module networks"

Connected through hyperedge HE-5 (induced by source paper "leveraging uncertainty in deep learning for selective classification"):
  with members:
    - "selective classification for deep neural networks"
    - "concrete problems in ai safety"

Connected through hyperedge HE-6 (induced by source paper "textproposals a text specific selective search algorithm for word spotting in the wild"):
  with members:
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"
    - "reading text in the wild with convolutional neural networks"
    - "robust text detection in natural scene images"
    - "end to end text recognition with hybrid hmm maxout models"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "utility theoretic ranking for semiautomated text classification"):
  with members:
    - "machine learning in automated text categorization"
    - "identifying mislabeled training data"

Connected through hyperedge HE-8 (induced by source paper "active learning for delineation of curvilinear structures"):
  with members:
    - "hierarchical subquery evaluation for active learning on a graph"
    - "active learning with statistical models"

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)