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: "adversarial regression for detecting attacks in cyber physical systems"
  Abstract: Attacks in cyber-physical systems (CPS) which manipulate sensor readings can cause enormous physical damage if undetected. Detection of attacks on sensors is crucial to mitigate this issue. We study supervised regression as a means to detect anomalous sensor readings, where each sensor's measurement is predicted as a function of other sensors. We show that several common learning approaches in this context are still vulnerable to \emph{stealthy attacks}, which carefully modify readings of compromised sensors to cause desired damage while remaining undetected. Next, we model the interaction between the CPS defender and attacker as a Stackelberg game in which the defender chooses detection thresholds, while the attacker deploys a stealthy attack in response. We present a heuristic algorithm for finding an approximately optimal threshold for the defender in this game, and show that it increases system resilience to attacks without significantly increasing the false alarm rate.

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"
    - "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"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-2 (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"
    - "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems"
    - "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"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "cyber physical systems security a survey"

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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a fundamental performance limitation for adversarial classification"):
  with members:
    - "adversarial attacks and defences competition"
    - "certified defenses against adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "adversarial machine learning at scale"
    - "the limitations of deep learning in adversarial settings"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "intriguing properties of neural networks"

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.AI(Artificial Intelligence)