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: "exploring information centrality for intrusion detection in large networks"
  Abstract: Modern networked systems are constantly under threat from systemic attacks. There has been a massive upsurge in the number of devices connected to a network as well as the associated traffic volume. This has intensified the need to better understand all possible attack vectors during system design and implementation. Further, it has increased the need to mine large data sets, analyzing which has become a daunting task. It is critical to scale monitoring infrastructures to match this need, but a difficult goal for the small and medium organization. Hence, there is a need to propose novel approaches that address the big data problem in security. Information Centrality (IC) labels network nodes with better vantage points for detecting network-based anomalies as central nodes and uses them for detecting a category of attacks called systemic attacks. The main idea is that since these central nodes already see a lot of information flowing through the network, they are in a good position to detect anomalies before other nodes. This research first dives into the importance of using graphs in understanding the topology and information flow. We then introduce the usage of information centrality, a centrality-based index, to reduce data collection in existing communication networks. Using IC-identified central nodes can accelerate outlier detection when armed with a suitable anomaly detection technique. We also come up with a more efficient way to compute Information centrality for large networks. Finally, we demonstrate that central nodes detect anomalous behavior much faster than other non-central nodes, given the anomalous behavior is systemic in nature.

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)