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: "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems"
  Abstract: Recently, a number of deep learning-based anomaly detection algorithms were proposed to detect attacks in dynamic industrial control systems. The detectors operate on measured sensor data, leveraging physical process models learned a priori. Evading detection by such systems is challenging, as an attacker needs to manipulate a constrained number of sensor readings in real-time with realistic perturbations according to the current state of the system. In this work, we propose a number of evasion attacks (with different assumptions on the attacker's knowledge), and compare the attacks' cost and efficiency against replay attacks. In particular, we show that a replay attack on a subset of sensor values can be detected easily as it violates physical constraints. In contrast, our proposed attacks leverage manipulated sensor readings that observe learned physical constraints of the system. Our proposed white box attacker uses an optimization approach with a detection oracle, while our black box attacker uses an autoencoder (or a convolutional neural network) to translate anomalous data into normal data. Our proposed approaches are implemented and evaluated on two different datasets pertaining to the domain of water distribution networks. We then demonstrated the efficacy of the real-time attack on a realistic testbed. Results show that the accuracy of the detection algorithms can be significantly reduced through real-time adversarial actions: for the BATADAL dataset, the attacker can reduce the detection accuracy from 0.6 to 0.14. In addition, we discuss and implement an Availability attack, in which the attacker introduces detection events with minimal changes of the reported data, in order to reduce confidence in the detector.

Connected through hyperedge HE-1 (induced by source paper "can t boil this frog robustness of online trained autoencoder based anomaly detectors to adversarial poisoning attacks"):
  with members:
    - "efficient cyber attacks detection in industrial control systems using lightweight neural networks"
    - "deep learning for anomaly detection a survey"
    - "intrusion detection for industrial control systems evaluation analysis and adversarial attacks"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "cyber physical systems security a survey"

Connected through hyperedge HE-2 (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"
    - "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"
    - "early stage malware prediction using recurrent neural networks"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-3 (induced by source paper "efficient cyber attacks detection in industrial control systems using lightweight neural networks"):
  with members:
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "cyberattack detection using deep generative models with variational inference"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "stealthy deception attacks against scada systems"

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)