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: "on the reverse engineering of the citadel botnet"
  Abstract: Citadel is an advanced information stealing malware that targets financial information. This malware poses a real threat against the confidentiality and integrity of personal and business data. Recently, a joint operation has been conducted by FBI and Microsoft Digital Crimes Unit in order to take down Citadel command-and-control servers. The operation caused some disruption in the botnet but has not stopped it completely. Due to the complex structure and advanced anti-reverse engineering techniques, the Citadel malware analysis process is challenging and time-consuming. This allows cyber criminals to carry on with their attacks while the analysis is still in progress. In this paper, we present the results of the Citadel reverse engineering and provide additional insights into the functionality, inner workings, and open source components of the malware. In order to accelerate the reverse engineering process, we propose a clone-based analysis methodology. Citadel is an offspring of a previously analyzed malware called Zeus. Thus, using the former as a reference, we can measure and quantify the similarities and differences of the new variant. Two types of code analysis techniques are provided in the methodology namely assembly to source code matching, and binary clone detection. The methodology can help reduce the number of functions that should be analyzed manually. The analysis results prove that the approach is promising in Citadel malware analysis. Furthermore, the same approach is applicable to similar malware analysis scenarios.

Connected through hyperedge HE-1 (induced by source paper "towards developing network forensic mechanism for botnet activities in the iot based on machine learning techniques"):
  with members:
    - "a survey of advances in botnet technologies"
    - "a hybrid feature selection for network intrusion detection systems central points"

Connected through hyperedge HE-2 (induced by source paper "economic factors of vulnerability trade and exploitation"):
  with members:
    - "comparing vulnerability severity and exploits using case control studies"

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)