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: "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
  Abstract: Ad and tracking blocking extensions are popular tools for improving web performance, privacy and aesthetics. Content blocking extensions typically rely on filter lists to decide whether a web request is associated with tracking or advertising, and so should be blocked. Millions of web users rely on filter lists to protect their privacy and improve their browsing experience. #R##N#Despite their importance, the growth and health of filter lists are poorly understood. Filter lists are maintained by a small number of contributors, who use a variety of undocumented heuristics to determine what rules should be included. Lists quickly accumulate rules, and rules are rarely removed. As a result, users' browsing experiences are degraded as the number of stale, dead or otherwise not useful rules increasingly dwarf the number of useful rules, with no attenuating benefit. An accumulation of "dead weight" rules also makes it difficult to apply filter lists on resource-limited mobile devices. #R##N#This paper improves the understanding of crowdsourced filter lists by studying EasyList, the most popular filter list. We find that EasyList has grown from several hundred rules, to well over 60,000 rules, during its 9-year history. We measure how EasyList affects web browsing by applying EasyList to a sample of 10,000 websites. We find that 90.16% of the resource blocking rules in EasyList provide no benefit to users in common browsing scenarios. We further use our changes in EasyList application rates to provide a taxonomy of the ways advertisers evade EasyList rules. Finally, we propose optimizations for popular ad-blocking tools, that allow EasyList to be applied on performance constrained mobile devices, and improve desktop performance by 62.5%, while preserving over 99% of blocking coverage.

Connected through hyperedge HE-1 (induced by source paper "measuring abuse in web push advertising"):
  with members:
    - "master of web puppets abusing web browsers for persistent and stealthy computation"
    - "tracing information flows between ad exchanges using retargeted ads"
    - "ad blocking a study on performance privacy and counter measures"
    - "dial one for scam a large scale analysis of technical support scams"
    - "adscape harvesting and analyzing online display ads"

Connected through hyperedge HE-2 (induced by source paper "keeping out the masses understanding the popularity and implications of internet paywalls"):
  with members:
    - "cookie synchronization everything you always wanted to know but were afraid to ask"
    - "no more chasing waterfalls a measurement study of the header bidding ad ecosystem"
    - "speedreader reader mode made fast and private"
    - "truth in web mining measuring the profitability and cost of cryptominers as a web monetization model"
    - "tracking the trackers towards understanding the mobile advertising and tracking ecosystem"

Connected through hyperedge HE-3 (induced by source paper "adversarial perceptual ad blocking meets adversarial machine learning"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "stateful detection of black box adversarial attacks"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "sentinet detecting physical attacks against deep learning systems"
    - "yolov3 an incremental improvement"
    - "fooling ocr systems with adversarial text images"
    - "adversarially robust generalization requires more data"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-4 (induced by source paper "percival making in browser perceptual ad blocking practical with deep learning"):
  with members:
    - "adversarial logit pairing"
    - "understanding visual ads by aligning symbols and objects using co attention"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial attacks on neural network policies"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "the future of ad blocking an analytical framework and new techniques"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "advise symbolism and external knowledge for decoding advertisements"

Connected through hyperedge HE-5 (induced by source paper "filter list generation for underserved regions"):
  with members:
    - "the blind men and the internet multi vantage point web measurements"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "the future of ad blocking an analytical framework and new techniques"
    - "ad blocking a study on performance privacy and counter measures"
    - "variations in tracking in relation to geographic location"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "investigating the effectiveness of web adblockers"):
  with members:
    - "adversarial perceptual ad blocking meets adversarial machine learning"
    - "tracking the pixels detecting web trackers via analyzing invisible pixels"
    - "the future of ad blocking an analytical framework and new techniques"
    - "ad blocking a study on performance privacy and counter measures"
    - "a first look at ad block detection a new arms race on the web"

Connected through hyperedge HE-7 (induced by source paper "another brick in the paywall the popularity and privacy implications of paywalls"):
  with members:
    - "cookie synchronization everything you always wanted to know but were afraid to ask"
    - "no more chasing waterfalls a measurement study of the header bidding ad ecosystem"
    - "speedreader reader mode made fast and private"
    - "truth in web mining measuring the profitability and cost of cryptominers as a web monetization model"
    - "tracking the trackers towards understanding the mobile advertising and tracking ecosystem"

Connected through hyperedge HE-8 (induced by source paper "a longitudinal analysis of online ad blocking blacklists"):
  with members:
    - "the chain of implicit trust an analysis of the web third party resources loading"
    - "variations in tracking in relation to geographic location"
    - "exposing the hidden web an analysis of third party http requests on 1 million websites"

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)