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 "a stackelberg game perspective on the conflict between machine learning and data obfuscation"):
  Abstract: Data is the new oil; this refrain is repeated extensively in the age of internet tracking, machine learning, and data analytics. Social network analysis, cookie-based advertising, and government surveillance are all evidence of the use of data for commercial and national interests. Public pressure, however, is mounting for the protection of privacy. Frameworks such as differential privacy offer machine learning algorithms methods to guarantee limits to information disclosure, but they are seldom implemented. Recently, however, developers have made significant efforts to undermine tracking through obfuscation tools that hide user characteristics in a sea of noise. These services highlight an emerging clash between tracking and data obfuscation. In this paper, we conceptualize this conflict through a dynamic game between users and a machine learning algorithm that uses empirical risk minimization. First, a machine learner declares a privacy protection level, and then users respond by choosing their own perturbation amounts. We study the interaction between the users and the learner using a Stackelberg game. The utility functions quantify accuracy using expected loss and privacy in terms of the bounds of differential privacy. In equilibrium, we find selfish users tend to cause significant utility loss to trackers by perturbing heavily, in a phenomenon reminiscent of public good games. Trackers, however, can improve the balance by proactively perturbing the data themselves. While other work in this area has studied privacy markets and mechanism design for truthful reporting of user information, we take a different viewpoint by considering both user and learner perturbation.

Includes members:
  Member "a game theoretic study on non monetary incentives in data analytics projects with privacy implications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prospect theoretic analysis of privacy preserving mechanism")
  Member "selling privacy at auction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guidelines for implementing and auditing differentially private systems")
  Member "differentially private empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaffold stochastic controlled averaging for federated learning")

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)