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: "shallow and deep networks intrusion detection system a taxonomy and survey"
  Abstract: Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems.

Connected through hyperedge HE-1 (induced by source paper "a compression based framework for the detection of anomalies in heterogeneous data sources"):
  with members:
    - "an adaptive system for detecting malicious queries in web attacks"
    - "predicting cyber events by leveraging hacker sentiment"
    - "automatic detection of malware generated domains with recurrent neural models"
    - "current challenges and future research areas for digital forensic investigation"
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-2 (induced by source paper "autoids auto encoder based method for intrusion detection system"):
  with members:
    - "end to end adversarial learning for intrusion detection in computer networks"
    - "adversarially learned one class classifier for novelty detection"
    - "avid adversarial visual irregularity detection"

Connected through hyperedge HE-3 (induced by source paper "sequence covering for efficient host based intrusion detection"):
  with members:
    - "kernel based sequential data anomaly detection in business process event logs"

Connected through hyperedge HE-4 (induced by source paper "adversarial attacks on cognitive self organizing networks the challenge and the way forward"):
  with members:
    - "experience driven networking a deep reinforcement learning based approach"
    - "divide denoise and defend against adversarial attacks"
    - "machine learning for networking workflow advances and opportunities"
    - "deep learning in mobile and wireless networking a survey"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "knowledge defined networking"
    - "why and how networks should run themselves"
    - "unsupervised machine learning for networking techniques applications and research challenges"

Connected through hyperedge HE-5 (induced by source paper "when deep learning meets security"):
  with members:
    - "artificial intelligence enabled software defined networking a comprehensive overview"
    - "early stage malware prediction using recurrent neural networks"
    - "detecting malicious powershell commands using deep neural networks"
    - "deep neural networks for bot detection"
    - "iot security techniques based on machine learning"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "the rise of social bots"

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:
    - "machine learning approach for detection of nontor traffic"
    - "threat analysis of iot networks using artificial neural network intrusion detection system"
    - "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 "a taxonomy of malicious traffic for intrusion detection systems"):
  with members:
    - "sequence covering for efficient host based intrusion detection"
    - "glop enabling massively parallel incident response through gpu log processing"

Connected through hyperedge HE-8 (induced by source paper "unsupervised machine learning for networking techniques applications and research challenges"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "an introduction to deep learning for the physical layer"
    - "iot data analytics using deep learning"
    - "from 4g to 5g self organized network management meets machine learning"
    - "why and how networks should run themselves"
    - "a deep learning based ddos detection system in software defined networking sdn"

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)