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: "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"
  Abstract: We present a method that "meta" classifies whether segments (objects) predicted by a semantic segmentation neural network intersect with the ground truth. To this end, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input scene's size. We aggregate these dispersion measures segment-wise and derive metrics that are well-correlated with the segment-wise $\mathit{IoU}$ of prediction and ground truth. In our tests, we use two publicly available DeepLabv3+ networks (pre-trained on the Cityscapes data set) and analyze the predictive power of different metrics and different sets of metrics. To this end, we compute logistic LASSO regression fits for the task of classifying $\mathit{IoU}=0$ vs. $\mathit{IoU} > 0$ per segment and obtain classification rates of up to $81.91\%$ and AUROC values of up to $87.71\%$ without the incorporation of advanced techniques like Monte-Carlo dropout. We complement these tests with linear regression fits to predict the segment-wise $\mathit{IoU}$ and obtain prediction standard deviations of down to $0.130$ as well as $R^2$ values of up to $81.48\%$. We show that these results clearly outperform single-metric baseline approaches.

Connected through hyperedge HE-1 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems"):
  with members:
    - "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "the ethical dilemma when not setting up cost based decision rules in semantic segmentation"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "autoaugment learning augmentation policies from data"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "searching for efficient multi scale architectures for dense image prediction"

Connected through hyperedge HE-2 (induced by source paper "a tour of convolutional networks guided by linear interpreters"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "understanding deep architectures by visual summaries"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "the matrix calculus you need for deep learning"

Connected through hyperedge HE-3 (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"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"
    - "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-4 (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"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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-5 (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"
    - "classification uncertainty of deep neural networks based on gradient information"

Connected through hyperedge HE-6 (induced by source paper "application of decision rules for handling class imbalance in semantic segmentation"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "group equivariant convolutional networks"
    - "an overview of gradient descent optimization algorithms"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (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"
    - "classification uncertainty of deep neural networks based on gradient information"
    - "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"

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)