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 "optimal noise functions for location privacy on continuous regions"):
  Abstract: Users of location-based services are highly vulnerable to privacy risks since they need to disclose, at least partially, their locations to benefit from these services. One possibility to limit these risks is to obfuscate the location of a user by adding random noise drawn from a noise function. In this paper, we require the noise functions to satisfy a generic location privacy notion called #N#                  #N#                    #N#                  #N#                  $$\ell $$#N#                  #N#                    #N#                  #N#                -privacy, which makes the position of the user in a given region #N#                  #N#                    #N#                  #N#                  $$\mathcal {X}$$#N#                  #N#                    #N#                  #N#                 relatively indistinguishable from other points in #N#                  #N#                    #N#                  #N#                  $$\mathcal {X}$$#N#                  #N#                    #N#                  #N#                . We also aim at minimizing the loss in the service utility due to such obfuscation. While existing optimization frameworks regard the region #N#                  #N#                    #N#                  #N#                  $$\mathcal {X}$$#N#                  #N#                    #N#                  #N#                 restrictively as a finite set of points, we consider the more realistic case in which the region is rather continuous with a nonzero area. In this situation, we demonstrate that circular noise functions are enough to satisfy #N#                  #N#                    #N#                  #N#                  $$\ell $$#N#                  #N#                    #N#                  #N#                -privacy on #N#                  #N#                    #N#                  #N#                  $$\mathcal {X}$$#N#                  #N#                    #N#                  #N#                 and equivalently on the entire space without any penalty in the utility. Afterward, we describe a large parametric space of noise functions that satisfy #N#                  #N#                    #N#                  #N#                  $$\ell $$#N#                  #N#                    #N#                  #N#                -privacy on #N#                  #N#                    #N#                  #N#                  $$\mathcal {X}$$#N#                  #N#                    #N#                  #N#                , and show that this space has always an optimal member, regardless of #N#                  #N#                    #N#                  #N#                  $$\ell $$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\mathcal {X}$$#N#                  #N#                    #N#                  #N#                . We also investigate the recent notion of #N#                  #N#                    #N#                  #N#                  $$\epsilon $$#N#                  #N#                    #N#                  #N#                -geo-indistinguishability as an instance of #N#                  #N#                    #N#                  #N#                  $$\ell $$#N#                  #N#                    #N#                  #N#                -privacy and prove in this case that with respect to any increasing loss function, the planar Laplace noise function is optimal for any region having a nonzero area.

Includes members:
  Member "optimal geo indistinguishable mechanisms for location privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the long road to computational location privacy a survey")
  Member "optimal noise adding mechanisms for approximate differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reviewing and improving the gaussian mechanism for differential privacy")
  Member "a predictive differentially private mechanism for mobility traces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "local information privacy and its application to privacy preserving data aggregation")
  Member "impossibility of differentially private universally optimal mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "locally private hypothesis selection")
  Member "universally optimal privacy mechanisms for minimax agents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")
  Member "universally utility maximizing privacy mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guidelines for implementing and auditing differentially private systems")

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)