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: "smartphone fingerprinting via motion sensors analyzing feasiblity at large scale and studing real usage patterns"
  Abstract: Advertisers are increasingly turning to fingerprinting techniques to track users across the web. As web browsing activity shifts to mobile platforms, traditional browser fingerprinting techniques become less effective; however, device fingerprinting using built-in sensors offers a new avenue for attack. We study the feasibility of using motion sensors to perform device fingerprinting at scale, and explore countermeasures that can be used to protect privacy. #R##N#We perform a large-scale user study to demonstrate that motion sensor fingerprinting is effective with even 500 users. We also develop a model to estimate prediction accuracy for larger user populations; our model provides a conservative estimate of at least 12% classification accuracy with 100000 users. We then investigate the use of motion sensors on the web and find, distressingly, that many sites send motion sensor data to servers for storage and analysis, paving the way to potential fingerprinting. Finally, we consider the problem of developing fingerprinting countermeasures; we evaluate a previously proposed obfuscation technique and a newly developed quantization technique via a user study. We find that both techniques are able to drastically reduce fingerprinting accuracy without significantly impacting the utility of the sensors in web applications.

Connected through hyperedge HE-1 (induced by source paper "identifying ecus using inimitable characteristics of signals in controller area networks"):
  with members:
    - "device fingerprinting in wireless networks challenges and opportunities"
    - "mobile device identification via sensor fingerprinting"

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)