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: "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"
  Abstract: Composition is one of the most important properties of differential privacy (DP), as it allows algorithm designers to build complex private algorithms from DP primitives. We consider precise composition bounds of the overall privacy loss for exponential mechanisms, one of the fundamental classes of mechanisms in DP. We give explicit formulations of the optimal privacy loss for both the adaptive and non-adaptive settings. For the non-adaptive setting in which each mechanism has the same privacy parameter, we give an efficiently computable formulation of the optimal privacy loss. Furthermore, we show that there is a difference in the privacy loss when the exponential mechanism is chosen adaptively versus non-adaptively. To our knowledge, it was previously unknown whether such a gap existed for any DP mechanisms with fixed privacy parameters, and we demonstrate the gap for a widely used class of mechanism in a natural setting. We then improve upon the best previously known upper bounds for adaptive composition of exponential mechanisms with efficiently computable formulations and show the improvement.

Connected through hyperedge HE-1 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale"):
  with members:
    - "practical differentially private top k selection with pay what you get composition"
    - "pripearl a framework for privacy preserving analytics and reporting at linkedin"
    - "towards practical differential privacy for sql queries"
    - "collecting telemetry data privately"
    - "psi ψ a private data sharing interface"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-2 (induced by source paper "deep learning with gaussian differential privacy"):
  with members:
    - "hypothesis testing interpretations and renyi differential privacy"
    - "adaclip adaptive clipping for private sgd"
    - "gaussian differential privacy"
    - "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.CR(Cryptography and Security)