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 "detection of false positive and false negative samples in semantic segmentation"):
  Abstract: In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions.

Includes members:
  Member "towards neural networks that provably know when they don t know":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks")
  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-2 (induced by source paper "time dynamic estimates of the reliability of deep semantic segmentation networks")
  Member "time dynamic estimates of the reliability of deep semantic segmentation networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metafusion controlled false negative reduction of minority classes in semantic segmentation")
  Member "aleatoric and epistemic uncertainty in machine learning a tutorial introduction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bayesian neural networks with maximum mean discrepancy regularization")
  Member "the ethical dilemma when not setting up cost based decision rules in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems")
  Member "application of decision rules for handling class imbalance in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforced active learning for image segmentation")
  Member "leveraging uncertainty estimates for predicting segmentation quality":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity")
  Member "classification uncertainty of deep neural networks based on gradient information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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)