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: "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.

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)