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: "leveraging machine learning techniques for windows ransomware network traffic detection"
  Abstract: Ransomware has become a significant global threat with the ransomware-as-a-service model enabling easy availability and deployment, and the potential for high revenues creating a viable criminal business model. Individuals, private companies or public service providers e.g. healthcare or utilities companies can all become victims of ransomware attacks and consequently suffer severe disruption and financial loss. Although machine learning algorithms are already being used to detect ransomware, variants are being developed to specifically evade detection when using dynamic machine learning techniques. In this paper we introduce NetConverse, a machine learning evaluation study for consistent detection of Windows ransomware network traffic. Using a dataset created from conversation-based network traffic features we achieved a True Positive Rate (TPR) of 97.1% using the Decision Tree (J48) classifier.

Connected through hyperedge HE-1 (induced by source paper "towards resilient machine learning for ransomware detection"):
  with members:
    - "deep learning for malicious flow detection"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting adversarial examples via neural fingerprinting"
    - "adversarial logit pairing"
    - "deep transfer learning for static malware classification"
    - "generating adversarial examples with adversarial networks"
    - "black box adversarial attacks with limited queries and information"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"

Connected through hyperedge HE-2 (induced by source paper "cyber threat intelligence challenges and opportunities"):
  with members:
    - "leveraging support vector machine for opcode density based detection of crypto ransomware"
    - "emerging from the cloud a bibliometric analysis of cloud forensics studies"
    - "a model for android and ios applications risk calculation cvss analysis and enhancement using case control studies"
    - "adaptive traffic fingerprinting for darknet threat intelligence"
    - "machine learning aided static malware analysis a survey and tutorial"

Connected through hyperedge HE-3 (induced by source paper "leveraging support vector machine for opcode density based detection of crypto ransomware"):
  with members:
    - "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"
    - "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)