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 "on the power of multiple anonymous messages"):
  Abstract: An exciting new development in differential privacy is the shuffled model, in which an anonymous channel enables circumventing the large errors that are necessary in the local model, while relying on much weaker trust assumptions than in the central model. In this paper, we study basic counting problems in the shuffled model and establish separations between the error that can be achieved in the single-message shuffled model and in the shuffled model with multiple messages per user. For the frequency estimation problem with $n$ users and for a domain of size $B$, we obtain: #R##N#- A nearly tight lower bound of $\tilde{\Omega}( \min(n^{1/4}, \sqrt{B}))$ on the error in the single-message shuffled model. This implies that the protocols obtained from the amplification via shuffling work of Erlingsson et al. (SODA 2019) and Balle et al. (Crypto 2019) are essentially optimal for single-message protocols. #R##N#- A nearly tight lower bound of $\Omega\left(\frac{\log{B}}{\log\log{B}}\right)$ on the sample complexity with constant relative error in the single-message shuffled model. This improves on the lower bound of $\Omega(\log^{1/17} B)$ obtained by Cheu et al. (Eurocrypt 2019). #R##N#- Protocols in the multi-message shuffled model with $\mathrm{poly}(\log{B}, \log{n})$ bits of communication per user and $\mathrm{poly}\log{B}$ error, which provide an exponential improvement on the error compared to what is possible with single-message algorithms. #R##N#For the related selection problem, we also show a nearly tight sample complexity lower bound of $\Omega(B)$ in the single-message shuffled model. This improves on the $\Omega(B^{1/17})$ lower bound obtained by Cheu et al. (Eurocrypt 2019), and when combined with their $\tilde{O}(\sqrt{B})$-error multi-message algorithm, implies the first separation between single-message and multi-message protocols for this problem.

Includes members:
  Member "improved summation from shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "separating local shuffled differential privacy via histograms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "the privacy blanket of the shuffle model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "differentially private summation with multi message shuffling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "practical and robust privacy amplification with multi party differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private aggregation from fewer anonymous messages")
  Member "communication complexity in locally private distribution estimation and heavy hitters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "private heavy hitters and range queries in the shuffled model")
  Member "private aggregation from fewer anonymous messages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "scalable and differentially private distributed aggregation in the shuffled model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pure differentially private summation from anonymous messages")

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)