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: "improved summation from shuffling"
  Abstract: A protocol by Ishai et al.\ (FOCS 2006) showing how to implement distributed $n$-party summation from secure shuffling has regained relevance in the context of the recently proposed \emph{shuffle model} of differential privacy, as it allows to attain the accuracy levels of the curator model at a moderate communication cost. To achieve statistical security $2^{-\sigma}$, the protocol by Ishai et al.\ requires the number of messages sent by each party to {\em grow} logarithmically with $n$ as $O(\log n + \sigma)$. In this note we give an improved analysis achieving a dependency of the form $O(1+\sigma/\log n)$. Conceptually, this addresses the intuitive question left open by Ishai et al.\ of whether the shuffling step in their protocol provides a "hiding in the crowd" amplification effect as $n$ increases. From a practical perspective, our analysis provides explicit constants and shows, for example, that the method of Ishai et al.\ applied to summation of $32$-bit numbers from $n=10^4$ parties sending $12$ messages each provides statistical security $2^{-40}$.

Connected through hyperedge HE-1 (induced by source paper "pure differentially private summation from anonymous messages"):
  with members:
    - "on the power of multiple anonymous messages"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated learning strategies for improving communication efficiency"
    - "amplification by shuffling from local to central differential privacy via anonymity"

Connected through hyperedge HE-2 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "hadamard response estimating distributions privately efficiently and with little communication"

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)