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: "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images"
  Abstract: In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center and presents these, all re-scaled to the same size, to a neural network for semantic segmentation. The resulting softmax outputs are then post processed such that we can investigate mean and variance over all image crops as well as mean and variance of uncertainty heat maps obtained from pixel-wise uncertainty measures, like the entropy, applied to each crop's softmax output. In our tests, we use the publicly available DeepLabv3+ MobilenetV2 network (trained on the Cityscapes dataset) and demonstrate that the incorporation of crops improves the quality of the prediction and that we obtain more reliable uncertainty measures. These are then aggregated over predicted segments for either classifying between IoU=0 and IoU>0 (meta classification) or predicting the IoU via linear regression (meta regression). The latter yields reliable performance estimates for segmentation networks, in particular useful in the absence of ground truth. For the task of meta classification we obtain a classification accuracy of $81.93\%$ and an AUROC of $89.89\%$. For meta regression we obtain an $R^2$ value of $84.77\%$. These results yield significant improvements compared to other approaches.

Connected through hyperedge HE-1 (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks"):
  with members:
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "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-2 (induced by source paper "metafusion controlled false negative reduction of minority classes in semantic segmentation"):
  with members:
    - "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"
    - "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-3 (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"
    - "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"
    - "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"

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)