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: "classification uncertainty of deep neural networks based on gradient information"
  Abstract: We study the quantification of uncertainty of Convolutional Neural Networks (CNNs) based on gradient metrics. Unlike the classical softmax entropy, such metrics gather information from all layers of the CNN. We show for the EMNIST digits data set that for several such metrics we achieve the same meta classification accuracy -- i.e. the task of classifying predictions as correct or incorrect without knowing the actual label -- as for entropy thresholding. We apply meta classification to unknown concepts (out-of-distribution samples) -- EMNIST/Omniglot letters, CIFAR10 and noise -- and demonstrate that meta classification rates for unknown concepts can be increased when using entropy together with several gradient based metrics as input quantities for a meta classifier. Meta classifiers only trained on the uncertainty metrics of known concepts, i.e. EMNIST digits, usually do not perform equally well for all unknown concepts. If we however allow the meta classifier to be trained on uncertainty metrics for some out-of-distribution samples, meta classification for concepts remote from EMNIST digits (then termed known unknowns) can be improved considerably.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "detection of false positive and false negative samples in semantic segmentation"):
  with members:
    - "towards neural networks that provably know when they don t know"
    - "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"
    - "time dynamic estimates of the reliability of deep semantic segmentation networks"
    - "aleatoric and epistemic uncertainty in machine learning a tutorial introduction"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"

Connected through hyperedge HE-3 (induced by source paper "density estimation in representation space to predict model uncertainty"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "do imagenet classifiers generalize to imagenet"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "conditional neural processes"
    - "evidential deep learning to quantify classification uncertainty"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "adversarial attacks on neural network policies"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-4 (induced by source paper "accurate layerwise interpretable competence estimation"):
  with members:
    - "evaluation of neural network uncertainty estimation with application to resource constrained platforms"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "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-5 (induced by source paper "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"):
  with members:
    - "application of decision rules for handling class imbalance in semantic segmentation"
    - "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-6 (induced by source paper "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"):
  with members:
    - "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

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)