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: "developing a k ary malware using blockchain"
  Abstract: Cyberattacks are nowadays moving rapidly. They are customized, multi-vector, staged in multiple flows and targeted. Moreover, new hacking playgrounds appeared to reach mobile network, modern architectures and smart cities. For that purpose, malware use different entry points and plug-ins. In addition, they are now deploying several techniques for obfuscation, camouflage and analysis resistance. On the other hand, antiviral protections are positioning innovative approaches exposing malicious indicators and anomalies, revealing assumptions of the limitations of the anti-antiviral mechanisms. Primarily, this paper exposes a state of art in computer virology and then introduces a new concept to create undetectable malware based on the blockchain technology. It summarizes techniques adopted by malicious software to avoid functionalities implemented for viral detection and presents the implementation of new viral techniques that leverage the blockchain network.

Connected through hyperedge HE-1 (induced by source paper "ransomware as a service using smart contracts and ipfs"):
  with members:
    - "hydras and ipfs a decentralised playground for malware"
    - "on the economics of ransomware"

Connected through hyperedge HE-2 (induced by source paper "hydras and ipfs a decentralised playground for malware"):
  with members:
    - "detecting dga domains with recurrent neural networks and side information"
    - "a survey on malicious domains detection through dns data analysis"
    - "deepdga adversarially tuned domain generation and detection"
    - "ipfs content addressed versioned p2p file system"

Connected through hyperedge HE-3 (induced by source paper "the curious case of machine learning in malware detection"):
  with members:
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "adaptive and scalable android malware detection through online learning"
    - "deepsign deep learning for automatic malware signature generation and classification"

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)