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: "automated detection and classification of cryptographic algorithms in binary programs through machine learning"
  Abstract: Threats from the internet, particularly malicious software (i.e., malware) often use cryptographic algorithms to disguise their actions and even to take control of a victim's system (as in the case of ransomware). Malware and other threats proliferate too quickly for the time-consuming traditional methods of binary analysis to be effective. By automating detection and classification of cryptographic algorithms, we can speed program analysis and more efficiently combat malware. #R##N#This thesis will present several methods of leveraging machine learning to automatically discover and classify cryptographic algorithms in compiled binary programs. #R##N#While further work is necessary to fully evaluate these methods on real-world binary programs, the results in this paper suggest that machine learning can be used successfully to detect and identify cryptographic primitives in compiled code. Currently, these techniques successfully detect and classify cryptographic algorithms in small single-purpose programs, and further work is proposed to apply them to real-world examples.

Connected through hyperedge HE-1 (induced by source paper "leveraging machine learning techniques for windows ransomware network traffic detection"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "cloudme forensics a case of big data forensic investigation"
    - "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
    - "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"
    - "exploit kits the production line of the cybercrime economy"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"

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)