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: "exploit kits the production line of the cybercrime economy"
  Abstract: The annual cost of Cybercrime to the global economy is estimated to be around 400 billion dollar in support of which Exploit Kits have been providing enabling technology.This paper reviews the recent developments in Exploit Kit capability and how these are being applied in practice.In doing so it paves the way for better understanding of the exploit kits economy that may better help in combatting them and considers industry preparedness to respond.

Connected through hyperedge HE-1 (induced by source paper "volenti non fit injuria ransomware and its victims"):
  with members:
    - "temporal graph offset reconstruction towards temporally robust graph representation learning"
    - "software defined networking based crypto ransomware detection using http traffic characteristics"
    - "on the economics of ransomware"
    - "node2vec scalable feature learning for networks"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "detecting malicious powershell commands using deep neural networks"):
  with members:
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "dynamic analysis of executables to detect and characterize malware"
    - "hijacking net to defend powershell"
    - "exploring the limits of language modeling"
    - "character level convolutional networks for text classification"
    - "deep neural network based malware detection using two dimensional binary program features"
    - "text understanding from scratch"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "automated detection and classification of cryptographic algorithms in binary programs through machine learning"

Connected through hyperedge HE-4 (induced by source paper "know abnormal find evil frequent pattern mining for ransomware threat hunting and intelligence"):
  with members:
    - "monet a user oriented behavior based malware variants detection system for android"

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)