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: "evaluation of machine learning algorithms for intrusion detection system"
  Abstract: Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.

Connected through hyperedge HE-1 (induced by source paper "detecting network anomalies using rule based machine learning within snmp mib dataset"):
  with members:
    - "detecting network anomalies using machine learning and snmp mib dataset with ip group"
    - "a proactive design to detect denial of service attacks using snmp mib icmp variables"
    - "exploiting snmp mib data to detect network anomalies using machine learning techniques"
    - "an empirical evaluation for the intrusion detection features based on machine learning and feature selection methods"

Connected through hyperedge HE-2 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey"):
  with members:
    - "improving network intrusion detection classiﬁers by non payload based exploit independent obfuscations an adversarial approach"
    - "detecting cyberattacks in industrial control systems using convolutional neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an adaptive system for detecting malicious queries in web attacks"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "exploring adversarial examples in malware detection"
    - "is feature selection secure against training data poisoning"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-3 (induced by source paper "network attacks anomaly detection using snmp mib interface parameters"):
  with members:
    - "an empirical evaluation for the intrusion detection features based on machine learning and feature selection methods"

Connected through hyperedge HE-4 (induced by source paper "intrusion detection mechanism using fuzzy rule interpolation"):
  with members:
    - "a hybrid feature selection method to improve performance of a group of classification algorithms"

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)