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: "leveraging uncertainty estimates for predicting segmentation quality"
  Abstract: The use of deep learning for medical imaging has seen tremendous growth in the research community. One reason for the slow uptake of these systems in the clinical setting is that they are complex, opaque and tend to fail silently. Outside of the medical imaging domain, the machine learning community has recently proposed several techniques for quantifying model uncertainty (i.e.~a model knowing when it has failed). This is important in practical settings, as we can refer such cases to manual inspection or correction by humans. In this paper, we aim to bring these recent results on estimating uncertainty to bear on two important outputs in deep learning-based segmentation. The first is producing spatial uncertainty maps, from which a clinician can observe where and why a system thinks it is failing. The second is quantifying an image-level prediction of failure, which is useful for isolating specific cases and removing them from automated pipelines. We also show that reasoning about spatial uncertainty, the first output, is a useful intermediate representation for generating segmentation quality predictions, the second output. We propose a two-stage architecture for producing these measures of uncertainty, which can accommodate any deep learning-based medical segmentation pipeline.

Connected through hyperedge HE-1 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "a less biased evaluation of out of distribution sample detectors"

Connected through hyperedge HE-2 (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks"):
  with members:
    - "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"
    - "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"
    - "fast online object tracking and segmentation a unifying approach"
    - "towards a better match in siamese network based visual object tracker"
    - "learning dynamic memory networks for object tracking"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-3 (induced by source paper "metafusion controlled false negative reduction of minority classes in semantic segmentation"):
  with members:
    - "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"
    - "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"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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"
    - "a systematic study of the class imbalance problem in convolutional neural networks"

Connected through hyperedge HE-4 (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"
    - "classification uncertainty of deep neural networks based on gradient information"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"

Connected through hyperedge HE-5 (induced by source paper "reducing uncertainty in undersampled mri reconstruction with active acquisition"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "k space deep learning for accelerated mri"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "energy based generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "evaluation of neural network uncertainty estimation with application to resource constrained platforms"):
  with members:
    - "towards increased trustworthiness of deep learning segmentation methods on cardiac mri"
    - "learning for single shot confidence calibration in deep neural networks through stochastic inferences"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "distance based confidence score for neural network classifiers"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "what do we need to build explainable ai systems for the medical domain"

Connected through hyperedge HE-7 (induced by source paper "did you miss the sign a false negative alarm system for traffic sign detectors"):
  with members:
    - "simultaneous traffic sign detection and boundary estimation using convolutional neural network"
    - "failure prediction for autonomous driving"
    - "introspective perception learning to predict failures in vision systems"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "prostate segmentation from 3d mri using a two stage model and variable input based uncertainty measure"):
  with members:
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

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.CV(Computer Vision and Pattern Recognition)