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: "a first look at ad block detection a new arms race on the web"
  Abstract: The rise of ad-blockers is viewed as an economic threat by online publishers, especially those who primarily rely on ad- vertising to support their services. To address this threat, publishers have started retaliating by employing ad-block detectors, which scout for ad-blocker users and react to them by restricting their content access and pushing them to whitelist the website or disabling ad-blockers altogether. The clash between ad-blockers and ad-block detectors has resulted in a new arms race on the web. In this paper, we present the first systematic measurement and analysis of ad-block detection on the web. We have designed and implemented a machine learning based tech- nique to automatically detect ad-block detection, and use it to study the deployment of ad-block detectors on Alexa top- 100K websites. The approach is promising with precision of 94.8% and recall of 93.1%. We characterize the spectrum of different strategies used by websites for ad-block detection. We find that most of publishers use fairly simple passive ap- proaches for ad-block detection. However, we also note that a few websites use third-party services, e.g. PageFair, for ad-block detection and response. The third-party services use active deception and other sophisticated tactics to de- tect ad-blockers. We also find that the third-party services can successfully circumvent ad-blockers and display ads on publisher websites.

Connected through hyperedge HE-1 (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"
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "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"

Connected through hyperedge HE-2 (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"
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "the future of ad blocking an analytical framework and new techniques"
    - "ad blocking a study on performance privacy and counter measures"

Connected through hyperedge HE-3 (induced by source paper "cookie synchronization everything you always wanted to know but were afraid to ask"):
  with members:
    - "tracing information flows between ad exchanges using retargeted ads"
    - "tracking the trackers towards understanding the mobile advertising and tracking ecosystem"
    - "i always feel like somebody s watching me measuring online behavioural advertising"

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)