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 "keeping out the masses understanding the popularity and implications of internet paywalls"):
  Abstract: Funding the production of quality online content is a pressing problem for content producers. The most common funding method, online advertising, is rife with well-known performance and privacy harms, and an intractable subject-agent conflict: many users do not want to see advertisements, depriving the site of needed funding.  Because of these negative aspects of advertisement-based funding, paywalls are an increasingly popular alternative for websites. This shift to a "pay-for-access" web is one that has potentially huge implications for the web and society. Instead of a system where information (nominally) flows freely, paywalls create a web where high quality information is available to fewer and fewer people, leaving the rest of the web users with less information, that might be also less accurate and of lower quality. Despite the potential significance of a move from an "advertising-but-open" web to a "paywalled" web, we find this issue understudied.  This work addresses this gap in our understanding by measuring how widely paywalls have been adopted, what kinds of sites use paywalls, and the distribution of policies enforced by paywalls. A partial list of our findings include that (i) paywall use is accelerating (2x more paywalls every 6 months), (ii) paywall adoption differs by country (e.g. 18.75% in US, 12.69% in Australia), (iii) paywalls change how users interact with sites (e.g. higher bounce rates, less incoming links), (iv) the median cost of an annual paywall access is $108 per site, and (v) paywalls are in general trivial to circumvent.  Finally, we present the design of a novel, automated system for detecting whether a site uses a paywall, through the combination of runtime browser instrumentation and repeated programmatic interactions with the site. We intend this classifier to augment future, longitudinal measurements of paywall use and behavior.

Includes members:
  Member "cookie synchronization everything you always wanted to know but were afraid to ask":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stop tracking me bro differential tracking of user demographics on hyper partisan websites")
  Member "no more chasing waterfalls a measurement study of the header bidding ad ecosystem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stop tracking me bro differential tracking of user demographics on hyper partisan websites")
  Member "speedreader reader mode made fast and private":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "another brick in the paywall the popularity and privacy implications of paywalls")
  Member "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "measuring abuse in web push advertising")
  Member "truth in web mining measuring the profitability and cost of cryptominers as a web monetization model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a first look at the crypto mining malware ecosystem a decade of unrestricted wealth")
  Member "tracking the trackers towards understanding the mobile advertising and tracking ecosystem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a federated learning approach for mobile packet classification")

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.CY(Computers and Society)