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 "protecting spatiotemporal event privacy in continuous location based services"):
  Abstract: Location privacy-preserving mechanisms (LPPMs) have been extensively studied for protecting users' location privacy by releasing a perturbed location to third parties such as location-based service providers. However, when a user's perturbed locations are released continuously, existing LPPMs may not protect the sensitive information about the user's spatiotemporal activities, such as "visited hospital in the last week" or "regularly commuting between Address 1 and Address 2" (it is easy to infer that Addresses 1 and 2 may be home and office), which we call it \textit{spatiotemporal event}. In this paper, we first formally define {spatiotemporal event} as Boolean expressions between location and time predicates, and then we define $ \epsilon $-\textit{spatiotemporal event privacy} by extending the notion of differential privacy. Second, to understand how much spatiotemporal event privacy that existing LPPMs can provide, we design computationally efficient algorithms to quantify the privacy leakage of state-of-the-art LPPMs when an adversary has prior knowledge of the user's initial probability over possible locations. It turns out that the existing LPPMs cannot adequately protect spatiotemporal event privacy. Third, we propose a framework, PriSTE, to transform an existing LPPM into one protecting spatiotemporal event privacy against adversaries with \textit{any} prior knowledge. Our experiments on real-life and synthetic data verified that the proposed method is effective and efficient.

Includes members:
  Member "the long road to computational location privacy a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "quantifying differential privacy in continuous data release under temporal correlations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "money cannot buy everything trading infinite location data streams with bounded individual privacy loss")
  Member "priste from location privacy to spatiotemporal event privacy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "protect edge privacy in path publishing with differential privacy")
  Member "when and where do you want to hide recommendation of location privacy preferences with local differential privacy":
    also connected through other hyperedges:
      (none)
  Member "trajectory recovery from ash user privacy is not preserved in aggregated mobility data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nowhere to hide cross modal identity leakage between biometrics and devices")
  Member "what does the crowd say about you evaluating aggregation based location privacy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans")
  Member "quantifying differential privacy under temporal correlations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "videodp a universal platform for video analytics with differential privacy")
  Member "privacy games optimal user centric data obfuscation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)