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: "machine learning methods for attack detection in the smart grid"
  Abstract: Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

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"
    - "anomaly detection for a water treatment system using unsupervised machine learning"

Connected through hyperedge HE-2 (induced by source paper "state of the art of cyber physical systems security an automatic control perspective"):
  with members:
    - "on redundant observability from security index to attack detection and resilient state estimation"
    - "detection of sensor attack and resilient state estimation for uniformly observable nonlinear systems having redundant sensors"
    - "analysis of stochastic switched systems with application to networked control under jamming attacks"
    - "coding schemes for securing cyber physical systems against stealthy data injection attacks"
    - "detecting integrity attacks on control systems using a moving target approach"
    - "a moving horizon hybrid stochastic game for secure control of cyber physical systems"
    - "modeling and performance evaluation of stealthy false data injection attacks on smart grid in the presence of corrupted measurements"
    - "state estimation with secrecy against eavesdroppers"

Connected through hyperedge HE-3 (induced by source paper "machine learning in iot security current solutions and future challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "machine learning ddos detection for consumer internet of things devices"
    - "ai based two stage intrusion detection for software defined iot networks"
    - "machine intelligence techniques for next generation context aware wireless networks"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"

Connected through hyperedge HE-4 (induced by source paper "a system level behavioral detection framework for compromised cps devices smart grid case"):
  with members:
    - "real time analysis of privacy un aware iot applications"
    - "aegis a context aware security framework for smart home systems"
    - "sensitive information tracking in commodity iot"
    - "detection of compromised smart grid devices with machine learning and convolution techniques"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "smart grid security threats challenges and solutions"

Connected through hyperedge HE-5 (induced by source paper "real time detection of hybrid and stealthy cyber attacks in smart grid"):
  with members:
    - "optimal jammer placement in wireless localization systems"
    - "worst case jamming on mimo gaussian channels"
    - "optimal data attacks on power grids leveraging detection measurement jamming"

Connected through hyperedge HE-6 (induced by source paper "machine learning for intelligent authentication in 5g and beyond wireless networks"):
  with members:
    - "machine learning for wireless connectivity and security of cellular connected uavs"
    - "learning aided physical layer authentication as an intelligent process"
    - "analysis of channel based user authentication by key less and key based approaches"

Connected through hyperedge HE-7 (induced by source paper "smart grid cyber attacks detection using supervised learning and heuristic feature selection"):
  with members:
    - "sparse attack construction and state estimation in the smart grid centralized and distributed models"

Connected through hyperedge HE-8 (induced by source paper "iot security techniques based on machine learning"):
  with members:
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "in network outlier detection in wireless sensor 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.LG(Machine Learning)