human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pure differentially private summation from anonymous messages"):
  Abstract: The shuffled (aka anonymous) model has recently generated significant interest as a candidate distributed privacy framework with trust assumptions better than the central model but with achievable errors smaller than the local model. We study pure differentially private (DP) protocols in the shuffled model for summation, a basic and widely used primitive: #R##N#- For binary summation where each of n users holds a bit as an input, we give a pure $\epsilon$-DP protocol for estimating the number of ones held by the users up to an error of $O_\epsilon(1)$, and each user sends $O_\epsilon(\log n)$ messages each of 1 bit. This is the first pure protocol in the shuffled model with error $o(\sqrt{n})$ for constant $\epsilon$. #R##N#Using this protocol, we give a pure $\epsilon$-DP protocol that performs summation of real numbers in $[0, 1]$ up to an error of $O_{\epsilon}(1)$, and where each user sends $O_{\epsilon}(\log^3 n)$ messages each of $O(\log\log n)$ bits. #R##N#- In contrast, we show that for any pure $\epsilon$-DP protocol for binary summation in the shuffled model having absolute error $n^{0.5-\Omega(1)}$, the per user communication has to be at least $\Omega_{\epsilon}(\sqrt{\log n})$ bits. This implies the first separation between the (bounded-communication) multi-message shuffled model and the central model, and the first separation between pure and approximate DP protocols in the shuffled model. #R##N#To prove our lower bound, we consider (a generalization of) the following question: given $\gamma$ in $(0, 1)$, what is the smallest m for which there are two random variables $X^0, X^1$ supported on $\{0, \dots ,m\}$ such that (i) the total variation distance between $X^0$ and $X^1$ is at least $1-\gamma$, and (ii) the moment generating functions of $X^0$ and $X^1$ are within a constant factor of each other everywhere? We show that the answer is $m = \Theta(\sqrt{\log(1/\gamma)})$.

Includes members:
  Member "on the power of multiple anonymous messages":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "private summation in the multi message shuffle model")
  Member "improved summation from shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the power of multiple anonymous messages")
  Member "separating local shuffled differential privacy via histograms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the power of multiple anonymous messages")
  Member "the privacy blanket of the shuffle model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "differentially private summation with multi message shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "private aggregation from fewer anonymous messages":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private summation in the multi message shuffle model")
  Member "scalable and differentially private distributed aggregation in the shuffled model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private summation in the multi message shuffle model")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

Question: Please tell me which class the source paper 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)