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 "enabling trust in deep learning models a digital forensics case study"):
  Abstract: Today, the volume of evidence collected per case is growing exponentially, to address this problem forensics investigators are looking for investigation process with tools built on new technologies like big data, cloud services, and Deep Learning (DL) techniques. Consequently, the accuracy of artifacts found also relies on the performance of techniques used, especially DL models. Recently, Deep Neural Nets (DNN) have achieved state of the art performance in the tasks of classification and recognition. In the context of digital forensics, DNN has been applied in the domains of cybercrime investigation such as child abuse investigations, malware classification, steganalysis and image forensics. However, the robustness of DNN models in the context of digital forensics is never studied before. Hence, in this research, we design and implement a domain-independent Adversary Testing Framework (ATF) to test security robustness of black-box DNN's. By using ATF, we also methodically test a commercially available DNN service used in forensic investigations and bypass the detection, where published methods fail in control settings.

Includes members:
  Member "cloudme forensics a case of big data forensic investigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging machine learning techniques for windows ransomware network traffic detection")
  Member "threat of adversarial attacks on deep learning in computer vision a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "large scale jpeg image steganalysis using hybrid deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning in steganography and steganalysis from 2015 to 2018")
  Member "autoencoder with recurrent neural networks for video forgery detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving the security of image manipulation detection through one and a half class multiple classification")
  Member "attacking automatic video analysis algorithms a case study of google cloud video intelligence api":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarially robust frame sampling with bounded irregularities")
  Member "axiomatic attribution for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "unsupervised machine learning for networking techniques applications and research challenges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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.CR(Cryptography and Security)