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: "early stage malware prediction using recurrent neural networks"
  Abstract: Static malware analysis is well-suited to endpoint anti-virus systems as it can be conducted quickly by examining the features of an executable piece of code and matching it to previously observed malicious code. However, static code analysis can be vulnerable to code obfuscation techniques. Behavioural data collected during file execution is more difficult to obfuscate, but takes a relatively long time to capture - typically up to 5 minutes, meaning the malicious payload has likely already been delivered by the time it is detected. In this paper we investigate the possibility of predicting whether or not an executable is malicious based on a short snapshot of behavioural data. We find that an ensemble of recurrent neural networks are able to predict whether an executable is malicious or benign within the first 5 seconds of execution with 94% accuracy. This is the first time general types of malicious file have been predicted to be malicious during execution rather than using a complete activity log file post-execution, and enables cyber security endpoint protection to be advanced to use behavioural data for blocking malicious payloads rather than detecting them post-execution and having to repair the damage.

Connected through hyperedge HE-1 (induced by source paper "intrusion detection for industrial control systems evaluation analysis and adversarial attacks"):
  with members:
    - "deep latent defence"
    - "efficient cyber attacks detection in industrial control systems using lightweight neural networks"
    - "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "adversarial patch"
    - "anomaly detection for water treatment system based on neural network with automatic architecture optimization"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-2 (induced by source paper "neurlux dynamic malware analysis without feature engineering"):
  with members:
    - "ember an open dataset for training static pe malware machine learning models"
    - "attention is all you need"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "learning transferable features with deep adaptation networks"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "a c lstm neural network for text classification"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "distillation for run time malware process detection and automated process killing"):
  with members:
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "learning fast and slow propedeutica for real time malware detection"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-4 (induced by source paper "system misuse detection via informed behavior clustering and modeling"):
  with members:
    - "recurrent neural network language models for open vocabulary event level cyber anomaly detection"
    - "lstm based system call language modeling and robust ensemble method for designing host based intrusion detection systems"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "when deep learning meets security"):
  with members:
    - "artificial intelligence enabled software defined networking a comprehensive overview"
    - "detecting malicious powershell commands using deep 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"

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)