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 "strategy to increase the safety of a dnn based perception for had systems"):
  Abstract: Safety is one of the most important development goals for highly automated driving (HAD) systems. This applies in particular to the perception function driven by Deep Neural Networks (DNNs). For these, large parts of the traditional safety processes and requirements are not fully applicable or sufficient. The aim of this paper is to present a framework for the description and mitigation of DNN insufficiencies and the derivation of relevant safety mechanisms to increase the safety of DNNs. To assess the effectiveness of these safety mechanisms, we present a categorization scheme for evaluation metrics.

Includes members:
  Member "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "context aware domain adaptation in semantic segmentation")
  Member "adversarial camera stickers a physical camera based attack on deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial data encryption")
  Member "sampling free epistemic uncertainty estimation using approximated variance propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "leveraging uncertainties for deep multi modal object detection in autonomous driving")
  Member "the ethical dilemma when not setting up cost based decision rules in semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metafusion controlled false negative reduction of minority classes in semantic segmentation")
  Member "prediction error meta classification in semantic segmentation detection via aggregated dispersion measures of softmax probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tour of convolutional networks guided by linear interpreters")
  Member "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep semi supervised anomaly detection")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")

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)