human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks"):
  Abstract: In the semantic segmentation of street scenes, the reliability of a prediction is of highest interest. The assessment of neural networks by means of uncertainties is a common ansatz to prevent safety issues. As in online applications like automated driving, a video stream of images is available, we present a time-dynamical approach to investigate uncertainties and assess the prediction quality of neural this http URL this end, we track segments over time and gather aggregated metrics per segment, e.g. mean dispersion metrics derived from the softmax output and segment sizes. Due to identifying segments over consecutive frames, we obtain time series of metrics from which we assess prediction quality. We do so by either classifying between intersection over union (IoU) = 0 and IoU > 0 (meta classification) or predicting the IoU directly (meta regression). In our tests, we analyze the influence of the length of the time series on the predictive power of metrics and study different models for meta classification and regression. We use two publicly available DeepLabv3+ networks as well as two street scene datasets, i.e., VIPER as a synthetic one and KITTI based on real data. We achieve classification accuracies of up to 81.20% and AUROC values of up to 88.68% for the task of meta classification. For meta regression we obtain $R^2$ values of up to 87.51%. We show that these results yield improvements compared to other approaches.

Includes members:
  Member "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "metafusion controlled false negative reduction of minority classes in semantic segmentation")
  Member "leveraging uncertainty estimates for predicting segmentation quality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity")
  Member "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uncertainty measures and prediction quality rating for the semantic segmentation of nested multi resolution street scene images")
  Member "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems")
  Member "fast online object tracking and segmentation a unifying approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "towards a better match in siamese network based visual object tracker":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spm tracker series parallel matching for real time visual object tracking")
  Member "learning dynamic memory networks for object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "efficient uncertainty estimation for semantic segmentation in videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable uncertainty for computer vision with functional variational inference")

Question: Please tell me which class the source paper 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)