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: "threat analysis of iot networks using artificial neural network intrusion detection system"
  Abstract: The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

Connected through hyperedge HE-1 (induced by source paper "poster abstract towards scalable and trustworthy decentralized collaborative intrusion detection system for iot"):
  with members:
    - "decentralized and collaborative ai on blockchain"
    - "ciota collaborative iot anomaly detection via blockchain"

Connected through hyperedge HE-2 (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-3 (induced by source paper "domain knowledge aided explainable artificial intelligence for intrusion detection and response"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "a parallel random forest algorithm for big data in a spark cloud computing environment"
    - "credit default mining using combined machine learning and heuristic approach"
    - "methods for interpreting and understanding deep neural networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "rationalizing neural predictions"
    - "the mythos of model interpretability"

Connected through hyperedge HE-4 (induced by source paper "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"):
  with members:
    - "towards the development of realistic botnet dataset in the internet of things for network forensic analytics bot iot dataset"
    - "adversarial and clean data are not twins"
    - "adversarial machine learning at scale"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards the development of realistic botnet dataset in the internet of things for network forensic analytics bot iot dataset"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques"
    - "lstm a search space odyssey"
    - "collaborative anomaly detection framework for handling big data of cloud computing"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-6 (induced by source paper "a taxonomy and survey of intrusion detection system design techniques network threats and datasets"):
  with members:
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "machine learning approach for detection of nontor traffic"
    - "glop enabling massively parallel incident response through gpu log processing"
    - "a new clustering approach for anomaly intrusion detection"
    - "novel intrusion detection using probabilistic neural network and adaptive boosting"

Connected through hyperedge HE-7 (induced by source paper "machine learning approach for detection of nontor traffic"):
  with members:
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "a highly efficient memory compression scheme for gpu accelerated intrusion detection systems"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

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.NE(Neural and Evolutionary Computing)