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: "from malware signatures to anti virus assisted attacks"
  Abstract: Although anti-virus software has significantly evolved over the last decade, classic signature matching based on byte patterns is still a prevalent concept for identifying security threats. Anti-virus signatures are a simple and fast detection mechanism that can complement more sophisticated analysis strategies. However, if signatures are not designed with care, they can turn from a defensive mechanism into an instrument of attack. In this paper, we present a novel method for automatically deriving signatures from anti-virus software and demonstrate how the extracted signatures can be used to attack sensible data with the aid of the virus scanner itself. We study the practicability of our approach using four commercial products and exemplarily discuss a novel attack vector made possible by insufficiently designed signatures. Our research indicates that there is an urgent need to improve pattern-based signatures if used in anti-virus software and to pursue alternative detection approaches in such products.

Connected through hyperedge HE-1 (induced by source paper "a novel malware detection system based on machine learning and binary visualization"):
  with members:
    - "wip are cracked applications really free an empirical analysis on android devices"

Connected through hyperedge HE-2 (induced by source paper "static malware detection subterfuge quantifying the robustness of machine learning and current anti virus"):
  with members:
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "reviewer integration and performance measurement for malware detection"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "evasion attacks against machine learning at test time"
    - "visualizing and understanding convolutional networks"
    - "intriguing properties of neural networks"

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)