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: "efficient uncertainty estimation for semantic segmentation in videos"
  Abstract: Uncertainty estimation in deep learning becomes more important recently. A deep learning model can't be applied in real applications if we don't know whether the model is certain about the decision or not. Some literature proposes the Bayesian neural network which can estimate the uncertainty by Monte Carlo Dropout (MC dropout). However, MC dropout needs to forward the model $N$ times which results in $N$ times slower. For real-time applications such as a self-driving car system, which needs to obtain the prediction and the uncertainty as fast as possible, so that MC dropout becomes impractical. In this work, we propose the region-based temporal aggregation (RTA) method which leverages the temporal information in videos to simulate the sampling procedure. Our RTA method with Tiramisu backbone is 10x faster than the MC dropout with Tiramisu backbone ($N=5$). Furthermore, the uncertainty estimation obtained by our RTA method is comparable to MC dropout's uncertainty estimation on pixel-level and frame-level metrics.

Connected through hyperedge HE-1 (induced by source paper "scalable uncertainty for computer vision with functional variational inference"):
  with members:
    - "deepfactors real time probabilistic dense monocular slam"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "neural rgb d sensing depth and uncertainty from a video camera"
    - "functional variational bayesian neural networks"
    - "sampling free epistemic uncertainty estimation using approximated variance propagation"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "evaluating bayesian deep learning methods for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "in defense of the triplet loss for person re identification"

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"
    - "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"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-4 (induced by source paper "sampling free epistemic uncertainty estimation using approximated variance propagation"):
  with members:
    - "inhibited softmax for uncertainty estimation in neural networks"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "sampling free uncertainty estimation in gated recurrent units with exponential families"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "noisy natural gradient as variational inference"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"

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"
    - "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"
    - "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 "improving semantic segmentation via video propagation and label relaxation"):
  with members:
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "adaptive affinity fields for semantic segmentation"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "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)