human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "detecting network anomalies using rule based machine learning within snmp mib dataset"):
  Abstract: One of the most effective threats that targeting cybercriminals to limit network performance is Denial of Service (DOS) attack. Thus, data security, completeness and efficiency could be greatly damaged by this type of attacks. This paper developed a network traffic system that relies on adopted dataset to differentiate the DOS attacks from normal traffic. The detection model is built with five Rule-based machine learning classifiers (DecisionTable, JRip, OneR, PART and ZeroR). The findings have shown that the ICMP variables are implemented in the identification of ICMP attack, HTTP flood attack, and Slowloris at a high accuracy of approximately 99.7% using PART classifier. In addition, PART classifier has succeeded in classifying normal traffic from different DOS attacks at 100%.

Includes members:
  Member "detecting network anomalies using machine learning and snmp mib dataset with ip group":
    also connected through other hyperedges:
      (none)
  Member "a proactive design to detect denial of service attacks using snmp mib icmp variables":
    also connected through other hyperedges:
      (none)
  Member "exploiting snmp mib data to detect network anomalies using machine learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attack based dos attack detection using multiple classifier")
  Member "evaluation of machine learning algorithms for intrusion detection system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey")
  Member "an empirical evaluation for the intrusion detection features based on machine learning and feature selection methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attack based dos attack detection using multiple classifier")

Question: Please tell me which class the source paper 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)