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 "under the hood of membership inference attacks on aggregate location time series"):
  Abstract: Aggregate location statistics are used in a number of mobility analytics to express how many people are in a certain location at a given time (but not who). However, prior work has shown that an adversary with some prior knowledge of a victim's mobility patterns can mount membership inference attacks to determine whether or not that user contributed to the aggregates. #R##N#In this paper, we set to understand why such inferences are successful and what can be done to mitigate them. We conduct an in-depth feature analysis, finding that the volume of data contributed and the regularity and particularity of mobility patterns play a crucial role in the attack. We then use these insights to adapt defenses proposed in the location privacy literature to the aggregate setting, and evaluate their privacy-utility trade-offs for common mobility analytics. We show that, while there is no silver bullet that enables arbitrary analysis, there are defenses that provide reasonable utility for particular tasks while reducing the extent of the inference.

Includes members:
  Member "on the lack of location privacy in crowdsourcing applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "privacy preserving multiple tensor factorization for synthesizing large scale location traces")
  Member "trajectory recovery from ash user privacy is not preserved in aggregated mobility data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "gan leaks a taxonomy of membership inference attacks against gans")
  Member "differentially private publication of location entropy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differential privacy for evolving almost periodic datasets with continual linear queries application to energy data privacy")
  Member "membership inference attacks against machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the future of digital health with federated learning")
  Member "privacy friendly mobility analytics using aggregate location data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards differentially private truth discovery for crowd sensing systems")
  Member "geo spotting mining online location based services for optimal retail store placement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards business partnership recommendation using user opinion on facebook")
  Member "on sampling anonymization and differential privacy or k anonymization meets differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "data dependent differentially private parameter learning for directed graphical models")

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)