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: "touchsignatures identification of user touch actions and pins based on mobile sensor data via javascript"
  Abstract: Conforming to W3C specifications, mobile web browsers allow JavaScript code in a web page to access motion and orientation sensor data without the user's permission. The associated risks to user security and privacy are however not considered in W3C specifications. In this work, for the first time, we show how user security can be compromised using these sensor data via browser, despite that the data rate is 3–5 times slower than what is available in app. We examine multiple popular browsers on Android and iOS platforms and study their policies in granting permissions to JavaScript code with respect to access to motion and orientation sensor data. Based on our observations, we identify multiple vulnerabilities, and propose TouchSignatures which implements an attack where malicious JavaScript code on an attack tab listens to such sensor data measurements. Based on these streams, TouchSignatures is able to distinguish the user's touch actions (i.e., tap, scroll, hold, and zoom) and her PINs, allowing a remote website to learn the client-side user activities. We demonstrate the practicality of this attack by collecting data from real users and reporting high success rates using our proof-of-concept implementations. We also present a set of potential solutions to address the vulnerabilities. The W3C community and major mobile browser vendors including Mozilla, Google, Apple and Opera have acknowledged our work and are implementing some of our proposed countermeasures.

Connected through hyperedge HE-1 (induced by source paper "zksense a privacy preserving mechanism for bot detection in mobile devices"):
  with members:
    - "batterylab a distributed power monitoring platform for mobile devices"
    - "protecting sensory data against sensitive inferences"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "systematic classification of side channel attacks a case study for mobile devices"):
  with members:
    - "stealing pins via mobile sensors actual risk versus user perception"
    - "no free charge theorem a covert channel via usb charging cable on mobile devices"
    - "the bumpy road towards iphone 5c nand mirroring"
    - "armageddon cache attacks on mobile devices"
    - "high precision fault injections on the instruction cache of armv7 m architectures"
    - "rowhammer js a remote software induced fault attack in javascript"
    - "a study on power side channels on mobile devices"
    - "drama exploiting dram addressing for cross cpu attacks"

Connected through hyperedge HE-3 (induced by source paper "replacement autoencoder a privacy preserving algorithm for sensory data analysis"):
  with members:
    - "self normalizing neural networks"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "privacy in the internet of things threats and challenges"
    - "pin skimming exploiting the ambient light sensor in mobile devices"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "stealing pins via mobile sensors actual risk versus user perception"):
  with members:
    - "exploring ways to mitigate sensor based smartphone fingerprinting"
    - "pin skimming exploiting the ambient light sensor in mobile devices"
    - "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.CR(Cryptography and Security)