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: "yerbabuena securing deep learning inference data via enclave based ternary model partitioning"
  Abstract: Deploying and serving deep learning (DL) models in the public cloud facilitates the process to bootstrap artificial intelligence (AI) services. Yet, preserving the confidentiality of sensitive input data remains a concern to most service users. Accidental disclosures of user input data may breach increasingly stringent data protection regulations and inflict reputation damage. In this paper, we systematically investigate the life cycles of input data in deep learning image classification pipelines and further identify the potential places for information disclosures. Based on the discovered insights, we build YerbaBuena, an enclave-based model serving system to protect the confidentiality and integrity of user input data. To accommodate the performance and capacity limitations of today's enclave technology, we employ a Ternary Model Partitioning strategy that allows service users to securely partition their proprietary DL models on local machines. Therefore, we can (I) enclose sensitive computation in a secure enclave to mitigate input information disclosures and (II) delegate non-sensitive workloads to run out of enclave with hardware-assisted DL acceleration. Our comprehensive partitioning analysis and workload measurement demonstrate how users can automatically determine the optimal partitioning for their models, thus to maximize confidentiality guarantees with low performance costs.

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)