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: "how private is your voting a framework for comparing the privacy of voting mechanisms"
  Abstract: Voting privacy has received a lot of attention across several research communities. Traditionally, cryptographic literature has focused on how to privately implement a voting mechanism. Yet, a number of recent works attempt to minimize the amount of information one can infer from the output (rather than the implementation) of the voting mechanism. These works apply differential privacy (DP) techniques which noise the outcome to achieve privacy. This approach intrinsically compromises accuracy, rendering such a voting mechanism unsuitable for most realistic scenarios. #R##N#In this work we investigate the inherent "noiseless" privacy that different voting rules achieve. To this end we utilize the well-accepted notion of Distributional Differential Privacy (DDP). We prove that under standard assumptions in voting literature about the distribution of votes, most natural mechanisms achieve a satisfactory level of DDP, indicating that noising--and its negative side-effects for voting--is unnecessary in most cases. #R##N#We then put forth a systematic study of noiseless privacy of commonly studied of voting rules, and compare these rules with respect to their privacy. Note that both DP and DDP induce (possibly loose) upper bounds on information leakage, which makes them insufficient for such a task. To circumvent this, we extend the definitions to require the bound to be exact (i.e. optimal) in a well defined manner. Although motivated by voting, our definitions and techniques can be generically applied to address the optimality (with respect to privacy) of general mechanisms for privacy-preserving data release.

Connected through hyperedge HE-1 (induced by source paper "differential privacy for eye tracking data"):
  with members:
    - "differential privacy and machine learning a survey and review"

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)