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: "hypothesis testing interpretations and renyi differential privacy"
  Abstract: Differential privacy is the gold standard in data privacy, with applications in the public and private sectors. While differential privacy is a formal mathematical definition from the theoretical computer science literature, it is also understood by statisticians and data experts thanks to its hypothesis testing interpretation. This informally says that one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism---any test cannot have both high significance and high power. #R##N#In this paper, we show that recently proposed relaxations of differential privacy based on Renyi divergence do not enjoy a similar interpretation. Specifically, we introduce the notion of $k$-generatedness for an arbitrary divergence, where the parameter $k$ captures the hypothesis testing complexity of the divergence. We show that the divergence used for differential privacy is 2-generated, and hence it satisfies the hypothesis testing interpretation. In contrast, Renyi divergence is only $\infty$-generated, and hence has no hypothesis testing interpretation. We also show sufficient conditions for general divergences to be $k$-generated.

Connected through hyperedge HE-1 (induced by source paper "a better bound gives a hundred rounds enhanced privacy guarantees via f divergences"):
  with members:
    - "privacy amplification by mixing and diffusion mechanisms"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "privacy amplification by iteration"
    - "renyi differential privacy"
    - "f divergence inequalities"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-2 (induced by source paper "deep learning with gaussian differential privacy"):
  with members:
    - "adaclip adaptive clipping for private sgd"
    - "gaussian differential privacy"
    - "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "efficient private erm for smooth objectives"
    - "collecting telemetry data privately"
    - "concentrated differential privacy"
    - "rappor randomized aggregatable privacy preserving ordinal response"

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.LG(Machine Learning)