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: "outis crypto assisted differential privacy on untrusted servers"
  Abstract: Differential privacy has steadily become the de-facto standard for achieving strong privacy guarantees in data analysis. It is typically implemented either in the "central" or "local" model. In the former, a trusted centralized server collects the records in the clear from the data owners and outputs differentially private statistics; while in the latter, the data owners individually randomize their inputs to ensure differential privacy. The local model has been popular as it dispenses with the need for a trusted data collector. This increased security in the local model, however, comes at the cost of strictly lower accuracy and restricted algorithmic expressibility for differentially private programs compared to the central model. In this work, we propose, Outis, a system for differential privacy that (1) eliminates the need for a trusted data collector like in the local model, but still (2) achieves the accuracy guarantees and algorithmic expressibility of DP programs of the central model. Outis achieves the "best of both worlds" by employing two non-colluding untrusted servers that run differentially private programs on encrypted data from data owners. Outis supports a rich class of differentially private programs that can be written in terms of a small set of data transformation and differentially private measurement primitives. Further, we propose optimizations that speed up computation on encrypted data by leveraging the fact that the final output is differentially private. We demonstrate the feasibility of Outis for practical differentially private analysis on untrusted servers with an extensive empirical evaluation on real datasets.

Connected through hyperedge HE-1 (induced by source paper "ara aggregated rappor and analysis for centralized differential privacy"):
  with members:
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "prochlo strong privacy for analytics in the crowd"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "what can we learn privately"

Connected through hyperedge HE-2 (induced by source paper "practical and robust privacy amplification with multi party differential privacy"):
  with members:
    - "separating local shuffled differential privacy via histograms"
    - "private heavy hitters and range queries in the shuffled model"
    - "the privacy blanket of the shuffle model"
    - "secure and utility aware data collection with condensed local differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "an algorithmic framework for differentially private data analysis on trusted processors"
    - "distributed differential privacy via shuffling"

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)