human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "differentially private trajectory data publication"
  Abstract: With the increasing prevalence of location-aware devices, trajectory data has been generated and collected in various application domains. Trajectory data carries rich in- formation that is useful for many data analysis tasks. Yet, improper publishing and use of trajectory data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving trajectory data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing trajectory data under the rigorous differential privacy model. We propose an efficient data-dependent yet differentially private sanitization algorithm, which is applicable to different types of trajectory data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree t o conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publi shing large volume of trajectory data under differential privacy. We examine the utility of sanitized data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life trajectory data from the STM demonstrate that our approach maintains high utility and is scalable to large trajectory datasets.

Connected through hyperedge HE-1 (induced by source paper "differential privacy an economic method for choosing epsilon"):
  with members:
    - "a theory of pricing private data"
    - "a market for unbiased private data paying individuals according to their privacy attitudes"
    - "calibrating data to sensitivity in private data analysis"
    - "conducting truthful surveys cheaply"
    - "privbasis frequent itemset mining with differential privacy"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "privacy preserving record linkage via grams projections"
    - "take it or leave it running a survey when privacy comes at a cost"

Connected through hyperedge HE-2 (induced by source paper "non interactive differential privacy a survey"):
  with members:
    - "small count privacy and large count utility in data publishing"
    - "calibrating data to sensitivity in private data analysis"
    - "privbasis frequent itemset mining with differential privacy"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "accurate and efficient private release of datacubes and contingency tables"
    - "fast private data release algorithms for sparse queries"
    - "differentially private search log sanitization with optimal output utility"
    - "a learning theory approach to non interactive database privacy"

Question: Please tell me which class the center node 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)