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: "detection of unauthorized iot devices using machine learning techniques"
  Abstract: Security experts have demonstrated numerous risks imposed by Internet of Things (IoT) devices on organizations. Due to the widespread adoption of such devices, their diversity, standardization obstacles, and inherent mobility, organizations require an intelligent mechanism capable of automatically detecting suspicious IoT devices connected to their networks. In particular, devices not included in a white list of trustworthy IoT device types (allowed to be used within the organizational premises) should be detected. In this research, Random Forest, a supervised machine learning algorithm, was applied to features extracted from network traffic data with the aim of accurately identifying IoT device types from the white list. To train and evaluate multi-class classifiers, we collected and manually labeled network traffic data from 17 distinct IoT devices, representing nine types of IoT devices. Based on the classification of 20 consecutive sessions and the use of majority rule, IoT device types that are not on the white list were correctly detected as unknown in 96% of test cases (on average), and white listed device types were correctly classified by their actual types in 99% of cases. Some IoT device types were identified quicker than others (e.g., sockets and thermostats were successfully detected within five TCP sessions of connecting to the network). Perfect detection of unauthorized IoT device types was achieved upon analyzing 110 consecutive sessions; perfect classification of white listed types required 346 consecutive sessions, 110 of which resulted in 99.49% accuracy. Further experiments demonstrated the successful applicability of classifiers trained in one location and tested on another. In addition, a discussion is provided regarding the resilience of our machine learning-based IoT white listing method to adversarial attacks.

Connected through hyperedge HE-1 (induced by source paper "iot device identification using deep learning"):
  with members:
    - "memory forensic analysis of mqtt devices"
    - "peek a boo i see your smart home activities even encrypted"

Connected through hyperedge HE-2 (induced by source paper "anomalous communications detection in iot networks using sparse autoencoders"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "distributed anomaly detection using autoencoder neural networks in wsn for iot"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "iot sentinel demo automated device type identification for security enforcement in iot"

Connected through hyperedge HE-3 (induced by source paper "verifying and monitoring iots network behavior using mud profiles"):
  with members:
    - "internet of things survey on security"

Connected through hyperedge HE-4 (induced by source paper "a survey of machine and deep learning methods for internet of things iot security"):
  with members:
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "machine learning ddos detection for consumer internet of things devices"
    - "lifelong learning for sentiment classification"
    - "iot security techniques based on machine learning"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deep learning for secure mobile edge computing"

Connected through hyperedge HE-5 (induced by source paper "peek a boo i see your smart home activities even encrypted"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "iotsense behavioral fingerprinting of iot devices"
    - "6thsense a context aware sensor based attack detector for smart devices"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

Connected through hyperedge HE-6 (induced by source paper "automatic device classification from network traffic streams of internet of things"):
  with members:
    - "the dark side channel of mobile devices a survey on network traffic analysis"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "toward secure edge networks taming device to device d2d communication in iot"):
  with members:
    - "smote synthetic minority over sampling technique"

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)