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: "a cyber kill chain based taxonomy of banking trojans for evolutionary computational intelligence"
  Abstract: Malware such as banking Trojans are popular with financially-motivated cybercriminals. Detection of banking Trojans remains a challenging task, due to the constant evolution of techniques used to obfuscate and circumvent existing detection and security solutions. Having a malware taxonomy can facilitate the design of mitigation strategies such as those based on evolutionary computational intelligence. Specifically, in this paper, we propose a cyber kill chain based taxonomy of banking Trojans features. This threat intelligence based taxonomy provides a stage-by-stage operational understanding of a cyber-attack, and can be highly beneficial to security practitioners and inform the design of evolutionary computational intelligence on Trojans detection and mitigation strategy. The proposed taxonomy is built upon our analysis of a real-world dataset of 127 banking Trojans collected from December 2014 to January 2016 by a major UK-based financial organization.

Connected through hyperedge HE-1 (induced by source paper "a blockchain based framework for detecting malicious mobile applications in app stores"):
  with members:
    - "empirical vulnerability analysis of automated smart contracts security testing on blockchains"
    - "a model for android and ios applications risk calculation cvss analysis and enhancement using case control studies"
    - "forensics analysis of android mobile voip apps"
    - "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"

Connected through hyperedge HE-2 (induced by source paper "an ai based multi stage detection system of banking botnets"):
  with members:
    - "predicting domain generation algorithms with long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "emerging from the cloud a bibliometric analysis of cloud forensics studies"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "internet of things security and forensics challenges and opportunities"
    - "forensic investigation of p2p cloud storage bittorrent sync as a case study"
    - "cloud storage forensics analysis of data remnants on spideroak justcloud and pcloud"
    - "investigating storage as a service cloud platform pcloud as a case study"
    - "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"
    - "cloud storage forensic hubic as a case study"
    - "calm before the storm the challenges of cloud computing in digital forensics"

Connected through hyperedge HE-4 (induced by source paper "a model for android and ios applications risk calculation cvss analysis and enhancement using case control studies"):
  with members:
    - "greening cloud enabled big data storage forensics syncany as a case study"
    - "internet of things security and forensics challenges and opportunities"
    - "mobile phone forensics an investigative framework based on user impulsivity and secure collaboration errors"
    - "forensics analysis of android mobile voip apps"
    - "an android cloud storage apps forensic taxonomy"
    - "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"
    - "forensic investigation of social media and instant messaging services in firefox os facebook twitter google telegram openwapp and line as case studies"

Connected through hyperedge HE-5 (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"
    - "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"
    - "automated detection and classification of cryptographic algorithms in binary programs through machine learning"

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)