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: "detecting malicious powershell commands using deep neural networks"
  Abstract: Microsoft's PowerShell is a command-line shell and scripting language that is installed by default on Windows machines. While PowerShell can be configured by administrators for restricting access and reducing vulnerabilities, these restrictions can be bypassed. Moreover, PowerShell commands can be easily generated dynamically, executed from memory, encoded and obfuscated, thus making the logging and forensic analysis of code executed by PowerShell challenging.For all these reasons, PowerShell is increasingly used by cybercriminals as part of their attacks' tool chain, mainly for downloading malicious contents and for lateral movement. Indeed, a recent comprehensive technical report by Symantec dedicated to PowerShell's abuse by cybercrimials reported on a sharp increase in the number of malicious PowerShell samples they received and in the number of penetration tools and frameworks that use PowerShell. This highlights the urgent need of developing effective methods for detecting malicious PowerShell commands.In this work, we address this challenge by implementing several novel detectors of malicious PowerShell commands and evaluating their performance. We implemented both "traditional" natural language processing (NLP) based detectors and detectors based on character-level convolutional neural networks (CNNs). Detectors' performance was evaluated using a large real-world dataset.Our evaluation results show that, although our detectors individually yield high performance, an ensemble detector that combines an NLP-based classifier with a CNN-based classifier provides the best performance, since the latter classifier is able to detect malicious commands that succeed in evading the former. Our analysis of these evasive commands reveals that some obfuscation patterns automatically detected by the CNN classifier are intrinsically difficult to detect using the NLP techniques we applied.

Connected through hyperedge HE-1 (induced by source paper "amsi based detection of malicious powershell code using contextual embeddings"):
  with members:
    - "attack2vec leveraging temporal word embeddings to understand the evolution of cyberattacks"
    - "ast based deep learning for detecting malicious powershell"
    - "neural classification of malicious scripts a study with javascript and vbscript"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "attention is all you need"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "scriptnet neural static analysis for malicious javascript detection"):
  with members:
    - "robust neural malware detection models for emulation sequence learning"
    - "convolutional sequence to sequence learning"
    - "a convolutional encoder model for neural machine translation"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "powerdrive accurate de obfuscation and analysis of powershell malware"):
  with members:
    - "ast based deep learning for detecting malicious powershell"
    - "hijacking net to defend powershell"

Connected through hyperedge HE-4 (induced by source paper "when deep learning meets security"):
  with members:
    - "artificial intelligence enabled software defined networking a comprehensive overview"
    - "early stage malware prediction using recurrent neural networks"
    - "deep neural networks for bot detection"
    - "iot security techniques based on machine learning"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "the rise of social bots"

Connected through hyperedge HE-5 (induced by source paper "ast based deep learning for detecting malicious powershell"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "on visual hallmarks of robustness to adversarial malware"
    - "building program vector representations for deep learning"

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)