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: "prolonging the hide and seek game optimal trajectory privacy for location based services"
  Abstract: Human mobility is highly predictable. Individuals tend to only visit a few locations with high frequency, and to move among them in a certain sequence reflecting their habits and daily routine. This predictability has to be taken into account in the design of location privacy preserving mechanisms (LPPMs) in order to effectively protect users when they continuously expose their position to location-based services (LBSs). In this paper, we describe a method for creating LPPMs that are customized for a user's mobility profile taking into account privacy and quality of service requirements. By construction, our LPPMs take into account the sequential correlation across the user's exposed locations, providing the maximum possible trajectory privacy, i.e., privacy for the user's present location, as well as past and expected future locations. Moreover, our LPPMs are optimal against a strategic adversary, i.e., an attacker that implements the strongest inference attack knowing both the LPPM operation and the user's mobility profile. The optimality of the LPPMs in the context of trajectory privacy is a novel contribution, and it is achieved by formulating the LPPM design problem as a Bayesian Stackelberg game between the user and the adversary. An additional benefit of our formal approach is that the design parameters of the LPPM are chosen by the optimization algorithm.

Connected through hyperedge HE-1 (induced by source paper "protecting spatiotemporal event privacy in continuous location based services"):
  with members:
    - "the long road to computational location privacy a survey"
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "priste from location privacy to spatiotemporal event privacy"
    - "when and where do you want to hide recommendation of location privacy preferences with local differential privacy"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "quantifying differential privacy under temporal correlations"
    - "privacy games optimal user centric data obfuscation"

Connected through hyperedge HE-2 (induced by source paper "impact of prior knowledge and data correlation on privacy leakage a unified analysis"):
  with members:
    - "information theory of data privacy"
    - "marginal release under local differential privacy"
    - "quantifying differential privacy under temporal correlations"
    - "strong and provably secure database access control"
    - "privacy preserving analysis of correlated data"
    - "on the differential privacy of bayesian inference"
    - "protecting locations with differential privacy under temporal correlations"
    - "blowfish privacy tuning privacy utility trade offs using policies"

Connected through hyperedge HE-3 (induced by source paper "on the privacy of dk random graphs"):
  with members:
    - "an efficient and robust social network de anonymization attack"
    - "privacy games optimal user centric data obfuscation"
    - "an efficient reconciliation algorithm for social networks"
    - "graph annotations in modeling complex network topologies"
    - "de anonymizing social networks"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-4 (induced by source paper "priste from location privacy to spatiotemporal event privacy"):
  with members:
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "protecting locations with differential privacy under temporal correlations"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-5 (induced by source paper "combinatorial algorithms for string sanitization"):
  with members:
    - "string sanitization a combinatorial approach"

Connected through hyperedge HE-6 (induced by source paper "rethinking location privacy for unknown mobility behaviors"):
  with members:
    - "back to the drawing board revisiting the design of optimal location privacy preserving mechanisms"
    - "privacy games optimal user centric data obfuscation"
    - "constructing elastic distinguishability metrics for location privacy"
    - "optimal geo indistinguishable mechanisms for location privacy"
    - "a predictive differentially private mechanism for mobility traces"
    - "geo indistinguishability differential privacy for location based systems"

Connected through hyperedge HE-7 (induced by source paper "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"):
  with members:
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "a mean field stackelberg game approach for obfuscation adoption in empirical risk minimization"
    - "quantitative models of imperfect deception in network security using signaling games with evidence"
    - "information leakage games"
    - "a stackelberg game perspective on the conflict between machine learning and data obfuscation"
    - "deception by design evidence based signaling games for network defense"
    - "privacy games optimal user centric data obfuscation"
    - "flip the cloud cyber physical signaling games in the presence of advanced persistent threats"

Connected through hyperedge HE-8 (induced by source paper "quantifying differential privacy under temporal correlations"):
  with members:
    - "protecting locations with differential privacy under temporal correlations"
    - "private decayed predicate sums on streams"
    - "calibrating data to sensitivity in private data analysis"
    - "web mining research a survey"

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)