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 "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.

Includes members:
  Member "an economic analysis of privacy protection and statistical accuracy as social choices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "mvg mechanism differential privacy under matrix valued query":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism")
  Member "amplification by shuffling from local to central differential privacy via anonymity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "ektelo a framework for defining differentially private computations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "shrinkwrap differentially private query processing in private data federations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "locally differentially private heavy hitter identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "marginal release under local differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private heavy hitters and range queries in the shuffled model")
  Member "cryptodl deep neural networks over encrypted data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crypto oriented neural architecture design")

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)