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: "stealing pins via mobile sensors actual risk versus user perception"
  Abstract: In this paper, we present the actual risks of stealing user PINs by using mobile sensors versus the perceived risks by users. First, we propose PINlogger.js which is a JavaScript-based side channel attack revealing user PINs on an Android mobile phone. In this attack, once the user visits a website controlled by an attacker, the JavaScript code embedded in the web page starts listening to the motion and orientation sensor streams without needing any permission from the user. By analysing these streams, it infers the user’s PIN using an artificial neural network. Based on a test set of fifty 4-digit PINs, PINlogger.js is able to correctly identify PINs in the first attempt with a success rate of 74% which increases to 86 and 94% in the second and third attempts, respectively. The high success rates of stealing user PINs on mobile devices via JavaScript indicate a serious threat to user security. With the technical understanding of the information leakage caused by mobile phone sensors, we then study users’ perception of the risks associated with these sensors. We design user studies to measure the general familiarity with different sensors and their functionality, and to investigate how concerned users are about their PIN being discovered by an app that has access to all these sensors. Our studies show that there is significant disparity between the actual and perceived levels of threat with regard to the compromise of the user PIN. We confirm our results by interviewing our participants using two different approaches, within-subject and between-subject, and compare the results. We discuss how this observation, along with other factors, renders many academic and industry solutions ineffective in preventing such side channel attacks.

Connected through hyperedge HE-1 (induced by source paper "magneticspy exploiting magnetometer in mobile devices for website and application fingerprinting"):
  with members:
    - "robust website fingerprinting through the cache occupancy channel"
    - "magneto covert channel between air gapped systems and nearby smartphones via cpu generated magnetic fields"
    - "perfweb how to violate web privacy with hardware performance events"
    - "a study on power side channels on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "cybercrime and you how criminals attack and the human factors that they seek to exploit"):
  with members:
    - "the group element of cybercrime types dynamics and criminal operations"
    - "privacy is the boring bit user perceptions and behaviour in the internet of things"

Connected through hyperedge HE-3 (induced by source paper "systematic classification of side channel attacks a case study for mobile devices"):
  with members:
    - "no free charge theorem a covert channel via usb charging cable on mobile devices"
    - "touchsignatures identification of user touch actions and pins based on mobile sensor data via javascript"
    - "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-4 (induced by source paper "snoopy sniffing your smartwatch passwords via deep sequence learning"):
  with members:
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "teaching machines to read and comprehend"
    - "recurrent neural network regularization"
    - "sequence to sequence learning with neural networks"

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)