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: "anomaly detection for a water treatment system using unsupervised machine learning"
  Abstract: In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 different attack scenarios. We find that our DNN generates fewer false positives than our one-class SVM while our SVM detects slightly more anomalies. Overall, our DNN has a slightly better F measure than our SVM. We discuss the characteristics of the DNN and one-class SVM used in this experiment, and compare the advantages and disadvantages of the two methods.

Connected through hyperedge HE-1 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems"):
  with members:
    - "textbugger generating adversarial text against real world applications"
    - "dynamic detection of false data injection attack in smart grid using deep learning"
    - "adversarial attacks and defences a survey"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"

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"
    - "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"
    - "early stage malware prediction using recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "efficient cyber attacks detection in industrial control systems using lightweight neural networks"):
  with members:
    - "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"
    - "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"
    - "stealthy deception attacks against scada systems"

Connected through hyperedge HE-4 (induced by source paper "learning guided network fuzzing for testing cyber physical system defences"):
  with members:
    - "detecting zero day controller hijacking attacks on the power grid with enhanced deep learning"
    - "assessing the effectiveness of attack detection at a hackfest on industrial control systems"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "log based anomaly detection of cps using a statistical method"
    - "a formal approach to cyber physical attacks"
    - "towards concolic testing for hybrid systems"
    - "towards learning and verifying invariants of cyber physical systems by code mutation"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-5 (induced by source paper "lstm based anomaly detection detection rules from extreme value theory"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
    - "recurrent neural radio anomaly detection"

Connected through hyperedge HE-6 (induced by source paper "detecting cyberattacks in industrial control systems using convolutional neural networks"):
  with members:
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-7 (induced by source paper "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"):
  with members:
    - "log based anomaly detection of cps using a statistical method"
    - "towards concolic testing for hybrid systems"
    - "towards learning and verifying invariants of cyber physical systems by code mutation"

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.LG(Machine Learning)