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 and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"
  Abstract: Convolutional Neural Networks (CNNs) are propelling advances in a range of different computer vision tasks such as object detection and object segmentation. Their success has motivated research in applications of such models for medical image analysis. If CNN-based models are to be helpful in a medical context, they need to be precise, interpretable, and uncertainty in predictions must be well understood. In this paper, we develop and evaluate recent advances in uncertainty estimation and model interpretability in the context of semantic segmentation of polyps from colonoscopy images. We evaluate and enhance several architectures of Fully Convolutional Networks (FCNs) for semantic segmentation of colorectal polyps and provide a comparison between these models. Our highest performing model achieves a 76.06\% mean IOU accuracy on the EndoScene dataset, a considerable improvement over the previous state-of-the-art.

Connected through hyperedge HE-1 (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"
    - "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 "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"
    - "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"
    - "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-3 (induced by source paper "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities"):
  with members:
    - "classification uncertainty of deep neural networks based on gradient information"
    - "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.CV(Computer Vision and Pattern Recognition)