human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a survey of electromagnetic side channel attacks and discussion on their case progressing potential for digital forensics"):
  Abstract: Abstract   The increasing prevalence of Internet of Things (IoT) devices has made it inevitable that their pertinence to digital forensic investigations will increase into the foreseeable future. These devices produced by various vendors often posses limited standard interfaces for communication, such as USB ports or WiFi/Bluetooth wireless interfaces. Meanwhile, with an increasing mainstream focus on the security and privacy of user data, built-in encryption is becoming commonplace in consumer-level computing devices, and IoT devices are no exception. Under these circumstances, a significant challenge is presented to digital forensic investigations where data from IoT devices needs to be analysed.  This work explores the electromagnetic (EM) side-channel analysis literature for the purpose of assisting digital forensic investigations on IoT devices. EM side-channel analysis is a technique where unintentional electromagnetic emissions are used for eavesdropping on the operations and data handling of computing devices. The non-intrusive nature of EM side-channel approaches makes it a viable option to assist digital forensic investigations as these attacks require, and must result in, no modification to the target device. The literature on various EM side-channel analysis attack techniques are discussed – selected on the basis of their applicability in IoT device investigation scenarios. The insight gained from the background study is used to identify promising future applications of the technique for digital forensic analysis on IoT devices – potentially progressing a wide variety of currently hindered digital investigations.

Includes members:
  Member "systematic classification of side channel attacks a case study for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "taxonomy and challenges of out of band signal injection attacks and defenses")
  Member "watch me but don t touch me contactless control flow monitoring via electromagnetic emanations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "application inference using machine learning based side channel analysis")
  Member "evaluation of digital forensic process models with respect to digital forensics as a service":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "methodology for the automated metadata based classification of incriminating digital forensic artefacts")
  Member "current challenges and future research areas for digital forensic investigation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a compression based framework for the detection of anomalies in heterogeneous data sources")
  Member "full duplex backscatter interference networks based on time hopping spread spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backscatter data collection with unmanned ground vehicle mobility management and power allocation")

Question: Please tell me which class the source paper 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)