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: "dynamic multi layer signature based intrusion detection system using mobile agents"
  Abstract: Intrusion detection systems have become a key component in ensuring the safety of systems and networks. As networks grow in size and speed continues to increase, it is crucial that efficient scalable techniques should be developed for IDS systems. Signature based detection is the most extensively used threat detection technique for Intrusion Detection Systems (IDS). One of the foremost challenges for signaturebased IDS systems is how to keep up with large volume of incoming traffic when each packet needs to be compared with every signature in the database. When an IDS cannot keep up with the traffic flood, all it can do is to drop packets, therefore, may miss potential attacks. This paper proposes a new model called Dynamic Multi-Layer Signature based IDS using Mobile Agents, which can detect imminent threats with very high success rate by dynamically and automatically creating and using small and efficient multiple databases, and at the same time, provide mechanism to update these small signature databases at regular intervals using Mobile Agents

Connected through hyperedge HE-1 (induced by source paper "restoring the privacy and confidentiality of users over mobile collaborative learning mcl environment"):
  with members:
    - "combining naive bayes and decision tree for adaptive intrusion detection"

Connected through hyperedge HE-2 (induced by source paper "discovery of malicious attacks to improve mobile collaborative learning mcl"):
  with members:
    - "combining naive bayes and decision tree for adaptive intrusion detection"

Connected through hyperedge HE-3 (induced by source paper "a human centric approach to group based context awareness"):
  with members:
    - "a security framework for soa applications in mobile environment"
    - "data security in mobile devices by geo locking"
    - "fuzzy aided application layer semantic intrusion detection system fasids"

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)