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 location privacy in practice"
  Abstract: With the wide adoption of handheld devices (e.g. smartphones, tablets) a large number of location-based services (also called LBSs) have flourished providing mobile users with real-time and contextual information on the move. Accounting for the amount of location information they are given by users, these services are able to track users wherever they go and to learn sensitive information about them (e.g. their points of interest including home, work, religious or political places regularly visited). A number of solutions have been proposed in the past few years to protect users location information while still allowing them to enjoy geo-located services. Among the most robust solutions are those that apply the popular notion of differential privacy to location privacy (e.g. Geo-Indistinguishability), promising strong theoretical privacy guarantees with a bounded accuracy loss. While these theoretical guarantees are attracting, it might be difficult for end users or practitioners to assess their effectiveness in the wild. In this paper, we carry on a practical study using real mobility traces coming from two different datasets, to assess the ability of Geo-Indistinguishability to protect users' points of interest (POIs). We show that a curious LBS collecting obfuscated location information sent by mobile users is still able to infer most of the users POIs with a reasonable both geographic and semantic precision. This precision depends on the degree of obfuscation applied by Geo-Indistinguishability. Nevertheless, the latter also has an impact on the overhead incurred on mobile devices resulting in a privacy versus overhead trade-off. Finally, we show in our study that POIs constitute a quasi-identifier for mobile users and that obfuscating them using Geo-Indistinguishability is not sufficient as an attacker is able to re-identify at least 63% of them despite a high degree of obfuscation.

Connected through hyperedge HE-1 (induced by source paper "the long road to computational location privacy a survey"):
  with members:
    - "technical privacy metrics a systematic survey"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "knock knock who s there membership inference on aggregate location data"
    - "location privacy in cognitive radio networks a survey"
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "adaptive location privacy with alp"
    - "constructing elastic distinguishability metrics for location privacy"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-2 (induced by source paper "perturbation methods for protection of sensitive location data smartphone travel survey case study"):
  with members:
    - "a perspective on the challenges and opportunities for privacy aware big transportation data"
    - "is geo indistinguishability what you are looking for"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-3 (induced by source paper "perturbation privacy for sensitive locations in transit data publication a case study of montreal trajet surveys"):
  with members:
    - "is geo indistinguishability what you are looking for"

Connected through hyperedge HE-4 (induced by source paper "differentially private counting of users spatial regions"):
  with members:
    - "beyond points and paths counting private bodies"
    - "differential privacy an economic method for choosing epsilon"
    - "geo indistinguishability differential privacy for location based systems"
    - "differentially private grids for geospatial data"
    - "differentially private spatial decompositions"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-5 (induced by source paper "beyond points and paths counting private bodies"):
  with members:
    - "differential privacy an economic method for choosing epsilon"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private grids for geospatial data"
    - "differentially private spatial decompositions"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-6 (induced by source paper "adaptive location privacy with alp"):
  with members:
    - "constructing elastic distinguishability metrics for location privacy"
    - "time distortion anonymization for the publication of mobility data with high utility"
    - "an automated social graph de anonymization technique"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-7 (induced by source paper "time distortion anonymization for the publication of mobility data with high utility"):
  with members:
    - "privacy preserving publication of mobility data with high utility"
    - "a predictive differentially private mechanism for mobility traces"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving publication of mobility data with high utility"):
  with members:
    - "geo indistinguishability differential privacy for location based systems"

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.CR(Cryptography and Security)