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 "utility optimized local differential privacy mechanisms for distribution estimation"):
  Abstract: LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal data (e.g., distribution underlying the data) while protecting users' privacy. Although LDP does not require a trusted third party, it regards all personal data equally sensitive, which causes excessive obfuscation hence the loss of utility. In this paper, we introduce the notion of ULDP (Utility-optimized LDP), which provides a privacy guarantee equivalent to LDP only for sensitive data. We first consider the setting where all users use the same obfuscation mechanism, and propose two mechanisms providing ULDP: utility-optimized randomized response and utility-optimized RAPPOR. We then consider the setting where the distinction between sensitive and non-sensitive data can be different from user to user. For this setting, we propose a personalized ULDP mechanism with semantic tags to estimate the distribution of personal data with high utility while keeping secret what is sensitive for each user. We show theoretically and experimentally that our mechanisms provide much higher utility than the existing LDP mechanisms when there are a lot of non-sensitive data. We also show that when most of the data are non-sensitive, our mechanisms even provide almost the same utility as non-private mechanisms in the low privacy regime.

Includes members:
  Member "blender enabling local search with a hybrid differential privacy model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the power of synergy in differential privacy combining a small curator with local randomizers")
  Member "one sided differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practical and robust privacy amplification with multi party differential privacy")
  Member "renyi differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "memguard defending against black box membership inference attacks via adversarial examples")
  Member "collecting telemetry data privately":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "pufferfish privacy mechanisms for correlated data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "minimax rates of estimating approximate differential privacy")
  Member "differential privacy as a mutual information constraint":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "privacy games optimal user centric data obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ensuring privacy in location based services a model based approach")

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.DB(Databases)