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: "digital forensic approaches for amazon alexa ecosystem"
  Abstract: Internet of Things devices such as the Amazon Echo are undoubtedly great sources of potential digital evidence due to their ubiquitous use and their always on mode of operation, constituting a human life black box. The Amazon Echo in particular plays a centric role for the cloud based intelligent virtual assistant Alexa developed by Amazon Lab126. The Alexa enabled wireless smart speaker is the gateway for all voice commands submitted to Alexa. Moreover, the IVA interacts with a plethora of compatible IoT devices and third party applications that leverage cloud resources. Understanding the complex cloud ecosystem that allows ubiquitous use of Alexa is paramount on supporting digital investigations when need raises. This paper discusses methods for digital forensics pertaining to the IVA Alexa ecosystem. The primary contribution of this paper consists of a new efficient approach of combining cloud native forensics with client side forensics, to support practical digital investigations. Based on a deep understanding of the targeted ecosystem, we propose a proof of concept tool, CIFT, that supports identification, acquisition and analysis of both native artifacts from the cloud and client centric artifacts from local devices.

Connected through hyperedge HE-1 (induced by source paper "smart home personal assistants a security and privacy review"):
  with members:
    - "intelligent virtual assistant knows your life"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "contextual slot carryover for disparate schemas"
    - "user perceptions of smart home iot privacy"
    - "understanding and mitigating the security risks of voice controlled third party skills on amazon alexa and google home"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "comparing speech and keyboard text entry for short messages in two languages on touchscreen phones"

Connected through hyperedge HE-2 (induced by source paper "iotdots a digital forensics framework for smart environments"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "advertising in the iot era vision and challenges"
    - "digital forensic investigation of cloud storage services"

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)