human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "deep detector health management under adversarial campaigns"):
  with members:
    - "gradient similarity an explainable approach to detect adversarial attacks against deep learning"
    - "learning to evade static pe machine learning malware models via reinforcement learning"
    - "exploring adversarial examples in malware detection"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "towards evaluating the robustness of neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (induced by source paper "finding rats in cats detecting stealthy attacks using group anomaly detection"):
  with members:
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
    - "adam a method for stochastic optimization"
    - "graph based anomaly detection and description a survey"

Question: Please tell me which class the center node 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)