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 boosting with random linear classifiers for protected user generated data"
  Abstract: User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, an online service provider (SP) can continuously record user-generated data and depend on various predictive models learned from the data to improve their services and revenue. SPs owning a large collection of user-generated data has raised privacy concerns. We present a privacy-preserving framework, SecureBoost, which allows users to submit encrypted or randomly masked data to SPs that want to learn predictive models but offload the responsibility of protecting sensitive data. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to simplify the design of the proposed privacy-preserving boosting protocols. A Cryptographic Service Provider (CSP) is used to assist an SP's processing, reducing the complexity of the protocol constructions while the leakage of information to the CSP is limited. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, the SP learns only the base models (i.e., RLCs) and the CSP learns only the weights of the base models. This separated parameter holding avoids any of the two parties from abusing the final model or conducting model-based attacks. We have conducted extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data.

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)