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: "performance comparison of intrusion detection systems and application of machine learning to snort system"
  Abstract: Abstract   This study investigates the performance of two open source intrusion detection systems (IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer networks. Snort and Suricata were installed on two different but identical computers and the performance was evaluated at 10 Gbps network speed. It was noted that Suricata could process a higher speed of network traffic than Snort with lower packet drop rate but it consumed higher computational resources. Snort had higher detection accuracy and was thus selected for further experiments. It was observed that the Snort triggered a high rate of false positive alarms. To solve this problem a Snort adaptive plug-in was developed. To select the best performing algorithm for Snort adaptive plug-in, an empirical study was carried out with different learning algorithms and Support Vector Machine (SVM) was selected. A hybrid version of SVM and Fuzzy logic produced a better detection accuracy. But the best result was achieved using an optimised SVM with firefly algorithm with FPR (false positive rate) as 8.6% and FNR (false negative rate) as 2.2%, which is a good result. The novelty of this work is the performance comparison of two IDSs at 10 Gbps and the application of hybrid and optimised machine learning algorithms to Snort.

Connected through hyperedge HE-1 (induced by source paper "dynamic and application aware provisioning of chained virtual security network functions"):
  with members:
    - "impact of processing resource sharing on the placement of chained virtual network functions"
    - "optimal orchestration of virtual network functions"
    - "network function virtualization state of the art and research challenges"
    - "specifying and placing chains of virtual network functions"

Connected through hyperedge HE-2 (induced by source paper "network intrusion detection based on lstm and feature embedding"):
  with members:
    - "persistent hidden states and nonlinear transformation for long short term memory"
    - "lstm a search space odyssey"
    - "context dependent word representation for neural machine translation"
    - "entity embeddings of categorical variables"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "malware squid a novel iot malware traffic analysis framework using convolutional neural network and binary visualisation"):
  with members:
    - "a novel malware detection system based on machine learning and binary visualization"
    - "malware dynamic analysis evasion techniques a survey"
    - "tensorflow a system for large scale machine learning"
    - "social contagion theory examining dynamic social networks and human behavior"
    - "happiness is assortative in online social networks"

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.NI(Networking and Internet Architecture)