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: "case study disclosure of indirect device fingerprinting in privacy policies"
  Abstract: Recent developments in online tracking make it harder for individuals to detect and block trackers. Some sites have deployed indirect tracking methods, which attempt to uniquely identify a device by asking the browser to perform a seemingly-unrelated task. One type of indirect tracking, Canvas fingerprinting, causes the browser to render a graphic recording rendering statistics as a unique identifier. In this work, we observe how indirect device fingerprinting methods are disclosed in privacy policies, and consider whether the disclosures are sufficient to enable website visitors to block the tracking methods. We compare these disclosures to the disclosure of direct fingerprinting methods on the same websites. #R##N#Our case study analyzes one indirect fingerprinting technique, Canvas fingerprinting. We use an existing automated detector of this fingerprinting technique to conservatively detect its use on Alexa Top 500 websites that cater to United States consumers, and we examine the privacy policies of the resulting 28 websites. Disclosures of indirect fingerprinting vary in specificity. None described the specific methods with enough granularity to know the website used Canvas fingerprinting. Conversely, many sites did provide enough detail about usage of direct fingerprinting methods to allow a website visitor to reliably detect and block those techniques. #R##N#We conclude that indirect fingerprinting methods are often difficult to detect and are not identified with specificity in privacy policies. This makes indirect fingerprinting more difficult to block, and therefore risks disturbing the tentative armistice between individuals and websites currently in place for direct fingerprinting. This paper illustrates differences in fingerprinting approaches, and explains why technologists, technology lawyers, and policymakers need to appreciate the challenges of indirect fingerprinting.

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.CY(Computers and Society)