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 games optimal user centric data obfuscation"):
  Abstract: Consider users who share their data (e.g., lo- cation) with an untrusted service provider to obtain a personalized (e.g., location-based) service. Data obfus- cation is a prevalent user-centric approach to protecting users' privacy in such systems: the untrusted entity only receives a noisy version of user's data. Perturbing data before sharing it, however, comes at the price of the users' utility (service quality) experience which is an in- separable design factor of obfuscation mechanisms. The entanglement of the utility loss and the privacy guaran- tee, in addition to the lack of a comprehensive notion of privacy, have led to the design of obfuscation mecha- nisms that are either suboptimal in terms of their utility loss, or ignore the user's information leakage in the past, or are limited to very specific notions of privacy which e.g., do not protect against adaptive inference attacks or the adversary with arbitrary background knowledge. In this paper, we design user-centric obfuscation mech- anisms that impose the minimum utility loss for guar- anteeing user's privacy. We optimize utility subject to a joint guarantee of dierential privacy (indistinguishabil- ity) and distortion privacy (inference error). This dou- ble shield of protection limits the information leakage through obfuscation mechanism as well as the posterior inference. We show that the privacy achieved through joint dierential-disto rtion mechanisms against optimal attacks is as large as the maximum privacy that can be achieved by either of these mechanisms separately. Their utility cost is also not larger than what either of the dif- ferential or distortion mechanisms imposes. We model the optimization problem as a leader-follower game be- tween the designer of obfuscation mechanism and the potential adversary, and design adaptive mechanisms that anticipate and protect against optimal inference al- gorithms. Thus, the obfuscation mechanism is optimal against any inference algorithm.

Includes members:
  Member "privacy tradeoffs in predictive analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "through a gender lens learning usage patterns of emojis from large scale android users")
  Member "optimal geo indistinguishable mechanisms for location privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the long road to computational location privacy a survey")
  Member "blowfish privacy tuning privacy utility trade offs using policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations")
  Member "prolonging the hide and seek game optimal trajectory privacy for location based services":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "protecting spatiotemporal event privacy in continuous location based services")
  Member "a predictive differentially private mechanism for mobility traces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the long road to computational location privacy a survey")
  Member "geo indistinguishability differential privacy for location based systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "locally private hypothesis selection")
  Member "the optimal mechanism in differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "free gap information from the differentially private sparse vector and noisy max mechanisms")

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)