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 "trisec training data unaware imperceptible security attacks on deep neural networks"):
  Abstract: Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference. Therefore, data poisoning attacks during inference (e.g., adversarial attacks) are becoming more popular. However, they do not consider the imperceptibility factor in their optimization algorithms, and can be detected by correlation and structural testing. Therefore, in this paper, we propose a novel methodology which automatically generates imperceptible attack images by using the back-propagation algorithm on pre-trained DNNs. We present a case study on traffic sign detection using the VGGNet and the German Traffic Sign Recognition Benchmarks dataset in an autonomous driving use case. Our results demonstrate that the generated attack images successfully perform misclassification while remaining imperceptible in both `subjective' and `objective' quality tests

Includes members:
  Member "a roadmap toward the resilient internet of things for cyber physical systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "red attack resource efficient decision based attack for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stateful detection of black box adversarial attacks")
  Member "security for machine learning based systems attacks and challenges during training and inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods")
  Member "manipulating machine learning poisoning attacks and countermeasures for regression learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "fademl understanding the impact of pre processing noise filtering on adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods")
  Member "poison frogs targeted clean label poisoning attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "potrojan powerful neural level trojan designs in deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backdoor attacks against transfer learning with pre trained deep learning models")

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.LG(Machine Learning)