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: "hijacking net to defend powershell"
  Abstract: With the rise of attacks using PowerShell in the recent months, there has not been a comprehensive solution for monitoring or prevention. Microsoft recently released the AMSI solution for PowerShell v5, however this can also be bypassed. This paper focuses on repurposing various stealthy runtime .NET hijacking techniques implemented for PowerShell attacks for defensive monitoring of PowerShell. It begins with a brief introduction to .NET and PowerShell, followed by a deeper explanation of various attacker techniques, which is explained from the perspective of the defender, including assembly modification, class and method injection, compiler profiling, and C based function hooking. Of the four attacker techniques that are repurposed for defensive real-time monitoring of PowerShell execution, intermediate language binary modification, JIT hooking, and machine code manipulation provide the best results for stealthy run-time interfaces for PowerShell scripting analysis.

Connected through hyperedge HE-1 (induced by source paper "powerdrive accurate de obfuscation and analysis of powershell malware"):
  with members:
    - "ast based deep learning for detecting malicious powershell"
    - "detecting malicious powershell commands using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "detecting malicious powershell commands using deep neural networks"):
  with members:
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "dynamic analysis of executables to detect and characterize malware"
    - "exploit kits the production line of the cybercrime economy"
    - "exploring the limits of language modeling"
    - "character level convolutional networks for text classification"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "text understanding from scratch"
    - "speech recognition with deep recurrent 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)