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: "measuring third party tracker power across web and mobile"
  Abstract: Third-party networks collect vast amounts of data about users via web sites and mobile applications. Consolidations among tracker companies can significantly increase their individual tracking capabilities, prompting scrutiny by competition regulators. Traditional measures of market share, based on revenue or sales, fail to represent the tracking capability of a tracker, especially if it spans both web and mobile. This paper proposes a new approach to measure the concentration of tracking capability, based on the reach of a tracker on popular websites and apps. Our results reveal that tracker prominence and parent-subsidiary relationships have significant impact on accurately measuring concentration.

Connected through hyperedge HE-1 (induced by source paper "the price of free illegal live streaming services"):
  with members:
    - "movie pirates of the caribbean exploring illegal streaming cyberlockers"
    - "variations in tracking in relation to geographic location"

Connected through hyperedge HE-2 (induced by source paper "on privacy risks of public wifi captive portals"):
  with members:
    - "tranco a research oriented top sites ranking hardened against manipulation"
    - "web tracking mechanisms implications and defenses"

Connected through hyperedge HE-3 (induced by source paper "third party tracking in the mobile ecosystem"):
  with members:
    - "privacyproxy leveraging crowdsourcing and in situ traffic analysis to detect and mitigate information leakage"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "tracking the trackers towards understanding the mobile advertising and tracking ecosystem"
    - "exposing the hidden web an analysis of third party http requests on 1 million websites"
    - "an empirical study of mobile ad targeting"

Connected through hyperedge HE-4 (induced by source paper "are children well supported by their parents concerning online privacy risks and who supports the parents"):
  with members:
    - "tracking the trackers towards understanding the mobile advertising and tracking ecosystem"

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)