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: "assessing differentially private deep learning with membership inference"
  Abstract: Releasing data in the form of trained neural networks with differential privacy promises meaningful anonymization. However, there is an inherent privacy-accuracy trade-off in differential privacy which is challenging to assess for non-privacy experts. Furthermore, local and central differential privacy mechanisms are available to either anonymize the training data or the learnt neural network, and the privacy parameter $\epsilon$ cannot be used to compare these two mechanisms. We propose to measure privacy through a black-box membership inference attack and compare the privacy-accuracy trade-off for different local and central differential privacy mechanisms. Furthermore, we need to evaluate whether differential privacy is a useful mechanism in practice since differential privacy will especially be used by data scientists if membership inference risk is lowered more than accuracy. We experiment with several datasets and show that neither local differential privacy nor central differential privacy yields a consistently better privacy-accuracy trade-off in all cases. We also show that the relative privacy-accuracy trade-off, instead of strictly declining linearly over $\epsilon$, is only favorable within a small interval. For this purpose we propose $\varphi$, a ratio expressing the relative privacy-accuracy trade-off.

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)