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: "amsi based detection of malicious powershell code using contextual embeddings"
  Abstract: PowerShell is a command-line shell, supporting a scripting language. It is widely used in organizations for configuration management and task automation but is also increasingly used by cybercriminals for launching cyberattacks against organizations, mainly because it is pre-installed on Windows machines and exposes strong functionality that may be leveraged by attackers. This makes the problem of detecting malicious PowerShell code both urgent and challenging. Microsoft's Antimalware Scan Interface (AMSI) allows defending systems to scan all the code passed to scripting engines such as PowerShell prior to its execution. In this work, we conduct the first study of malicious PowerShell code detection using the information made available by AMSI. #R##N#We present several novel deep-learning based detectors of malicious PowerShell code that employ pretrained contextual embeddings of words from the PowerShell "language". A known problem in the cybersecurity domain is that labeled data is relatively scarce in comparison with unlabeled data, making it difficult to devise effective supervised detection of malicious activity of many types. This is also the case with PowerShell code. Our work shows that this problem can be mitigated by learning a pretrained contextual embedding based on unlabeled data. #R##N#We trained and evaluated our models using real-world data, collected using AMSI from a large antimalware vendor. Our performance analysis establishes that the use of unlabeled data for the embedding significantly improved the performance of our detectors. Our best-performing model uses an architecture that enables the processing of textual signals from both the character and token levels and obtains a true positive rate of nearly 90% while maintaining a low false-positive rate of less than 0.1%.

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)