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: "the future of ad blocking an analytical framework and new techniques"
  Abstract: We present a systematic study of ad blocking - and the associated "arms race" - as a security problem. We model ad blocking as a state space with four states and six state transitions, which correspond to techniques that can be deployed by either publishers or ad blockers. We argue that this is a complete model of the system. We propose several new ad blocking techniques, including ones that borrow ideas from rootkits to prevent detection by anti-ad blocking scripts. Another technique uses the insight that ads must be recognizable by humans to comply with laws and industry self-regulation. We have built prototype implementations of three of these techniques, successfully blocking ads and evading detection. We systematically evaluate our proposed techniques, along with existing ones, in terms of security, practicality, and legality. We characterize the order of growth of the development effort required to create/maintain ad blockers as a function of the growth of the web. Based on our state-space model, our new techniques, and this systematization, we offer insights into the likely "end game" of the arms race. We challenge the widespread assumption that the arms race will escalate indefinitely, and instead identify a combination of evolving technical and legal factors that will determine the outcome.

Connected through hyperedge HE-1 (induced by source paper "a4 evading learning based adblockers"):
  with members:
    - "filter list generation for underserved regions"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "adgraph a graph based approach to ad and tracker blocking"
    - "black box attacks against rnn based malware detection algorithms"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "percival making in browser perceptual ad blocking practical with deep learning"):
  with members:
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "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"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "advise symbolism and external knowledge for decoding advertisements"

Connected through hyperedge HE-4 (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"
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "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-5 (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"
    - "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-6 (induced by source paper "adgraph a graph based approach to ad and tracker blocking"):
  with members:
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "most websites don t need to vibrate a cost benefit approach to improving browser security"
    - "ad blocking a study on performance privacy and counter measures"

Connected through hyperedge HE-7 (induced by source paper "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"):
  with members:
    - "a long way to the top significance structure and stability of internet top lists"
    - "ad blocking a study on performance privacy and counter measures"
    - "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)