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: "negative selection approach application in network intrusion detection systems"
  Abstract: Abstract—Nature has always been an inspiration to researcherswith its diversity and robustness of its systems, and ArtiﬁcialImmune Systems are one of them. Many algorithms were inspiredby ongoing discoveries of biological immune systems techniquesand approaches. One of the basic and most common approachis the Negative Selection Approach, which is simple and easy toimplement. It was applied in many ﬁelds, but mostly in anomalydetection for the similarity of its basic idea. In this paper, areview is given on the application of negative selection approachin network security, speciﬁcally the intrusion detection system.As the work in this ﬁeld is limited, we need to understand whatthe challenges of this approach are. Recommendations are givenby the end of the paper for future work. I. I NTRODUCTION Networks are more vulnerable by time to intrusions andattacks, from inside and outside. Cyber-attacks are makingnews headlines worldwide, as threats to networks are gettingbolder and more sophisticated. Reports of 2011 and 2012are showing an increase in network attacks, with Denial ofService (DoS) and targeted attacks having a big share in it.As reported by many web sites like [1] [2] [3], ﬁgures 1 and 2show motivations behind attacks and targeted customer typesrespectively.Internal threats and Advanced Persistent Threats (APT)are the biggest threats to a network, as they are carefullyconstructed and dangerous, due to internal users’ privilegesto access network resources. Figure 3 shows internal networksecurity concerns. With this in mind, and the increasing so-phistication of attacks, new approaches to protect the networkresources are always under investigation, and the one thatis concerned with inside and outside threats is the IntrusionDetection System.Intrusion detection systems [4] [5] [6] have been around forquite some time, as a successful security system. An IntrusionDetection System (IDS) is a system that deﬁnes and detectspossible threats within a computer or a network, by gatheringand analysing information from the surrounding environment.

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)