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: "privacy preserving blockchain based federated learning with differential data sharing"
  Abstract: For the modern world where data is becoming one of the most valuable assets, robust data privacy policies rooted in the fundamental infrastructure of networks and applications are becoming an even bigger necessity to secure sensitive user data. In due course with the ever-evolving nature of newer statistical techniques infringing user privacy, machine learning models with algorithms built with respect for user privacy can offer a dynamically adaptive solution to preserve user privacy against the exponentially increasing multidimensional relationships that datasets create. Using these privacy aware ML Models at the core of a Federated Learning Ecosystem can enable the entire network to learn from data in a decentralized manner. By harnessing the ever-increasing computational power of mobile devices, increasing network reliability and IoT devices revolutionizing the smart devices industry, and combining it with a secure and scalable, global learning session backed by a blockchain network with the ability to ensure on-device privacy, we allow any Internet enabled device to participate and contribute data to a global privacy preserving, data sharing network with blockchain technology even allowing the network to reward quality work. This network architecture can also be built on top of existing blockchain networks like Ethereum and Hyperledger, this lets even small startups build enterprise ready decentralized solutions allowing anyone to learn from data across different departments of a company, all the way to thousands of devices participating in a global synchronized learning network.

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)