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 "privacy preserving multiple tensor factorization for synthesizing large scale location traces"):
  Abstract: With the widespread use of LBSs (Location-based Services), synthesizing location traces plays an increasingly important role in analyzing spatial big data while protecting users' privacy. Although location synthesizers have been widely studied, existing synthesizers do not provide utility, privacy, or scalability sufficiently, hence are not practical for large-scale location traces. To overcome this issue, we propose a novel location synthesizer called PPMTF (Privacy-Preserving Multiple Tensor Factorization). We model various statistical features of the original traces by a transition-count tensor and a visit-count tensor. We simultaneously factorize these two tensors via multiple tensor factorization, and train factor matrices via posterior sampling. Then we synthesize traces from reconstructed tensors using the MH (Metropolis-Hastings) algorithm. We comprehensively evaluate the proposed method using two datasets. Our experimental results show that the proposed method preserves various statistical features, provides plausible deniability, and synthesizes large-scale location traces in practical time. The proposed method also significantly outperforms the state-of-the-art with regard to the utility, privacy, and scalability.

Includes members:
  Member "local obfuscation mechanisms for hiding probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "local distribution obfuscation via probability coupling")
  Member "on the lack of location privacy in crowdsourcing applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "under the hood of membership inference attacks on aggregate location time series")
  Member "the long road to computational location privacy a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy aware time series data sharing with deep reinforcement learning")
  Member "evaluating differentially private machine learning in practice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "encode shuffle analyze privacy revisited formalizations and empirical evaluation")
  Member "utility optimized local differential privacy mechanisms for distribution estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism")
  Member "plausible deniability for privacy preserving data synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "fast differentially private matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certified data removal from machine learning models")
  Member "time distortion anonymization for the publication of mobility data with high utility":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the long road to computational location privacy a survey")

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)