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: "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
  Abstract: Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear classifier function fail in this regard as they produce almost always high confidence predictions far away from the training data. For bounded domains like images we propose a new robust optimization technique similar to adversarial training which enforces low confidence predictions far away from the training data. We show that this technique is surprisingly effective in reducing the confidence of predictions far away from the training data while maintaining high confidence predictions and test error on the original classification task compared to standard training.

Connected through hyperedge HE-1 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation"):
  with members:
    - "a benchmark for anomaly segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "mask scoring r cnn"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "an alarm system for segmentation algorithm based on shape model"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"

Connected through hyperedge HE-2 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks"):
  with members:
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "towards neural networks that provably know when they don t know"
    - "correlation via synthesis end to end nodule image generation and radiogenomic map learning based on generative adversarial network"
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "generative adversarial network in medical imaging a review"
    - "batch normalization is a cause of adversarial vulnerability"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"

Connected through hyperedge HE-3 (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-4 (induced by source paper "safety concerns and mitigation approaches regarding the use of deep learning in safety critical perception tasks"):
  with members:
    - "hardening of artificial neural networks for use in safety critical applications a mapping study"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "on physical adversarial patches for object detection"
    - "scaling provable adversarial defenses"
    - "adversarial patch"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-5 (induced by source paper "fast predictive uncertainty for classification with bayesian deep networks"):
  with members:
    - "backpack packing more into backprop"
    - "deep learning for classical japanese literature"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "evidential deep learning to quantify classification uncertainty"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-6 (induced by source paper "dropout strikes back improved uncertainty estimation via diversity sampled implicit ensembles"):
  with members:
    - "maximizing overall diversity for improved uncertainty estimates in deep ensembles"
    - "subspace inference for bayesian deep learning"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "dropout based active learning for regression"
    - "on calibration of modern neural networks"
    - "deep bayesian active learning with image data"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-7 (induced by source paper "safe robot navigation via multi modal anomaly detection"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "anomaly detection using deep learning based image completion"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "density estimation using real nvp"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "transparency motivations and challenges"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"

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)