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 secure and robust scheme for sharing confidential information in iot systems"
  Abstract: Abstract   In Internet of Things (IoT) systems with security demands, there is often a need to distribute sensitive information (such as encryption keys, digital signatures, or login credentials etc.) to the devices, so that it can be retrieved for confidential purposes at a later moment. However, this piece of information cannot be entrusted to any individual device, since the malfunction of one device will jeopardize the security of the entire network. Even if the information is split among the devices, there is still a danger when attackers compromise a group of them. Therefore we have designed and implemented a secure and robust scheme to facilitate the sharing of sensitive information in IoT networks. This solution provides two important features: 1) This scheme uses Threshold Secret Sharing (TSS) to split the information into shares to be kept by all devices in the system. And so the information can only be retrieved collaboratively by groups of devices. 2) This scheme ensures the privacy and integrity of that piece of information even when there is a large amount of sophisticated and collusive attackers who can hijack the devices. It is able to identify all the compromised devices, while still keeping the secret unknown and unforgeable to attackers.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a tutorial on computing t closeness"
    - "privacy preserving deep learning via weight transmission"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "building scalable decentralized payment systems"
    - "chiron privacy preserving machine learning as a service"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"

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)