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 "peek a boo i see your smart home activities even encrypted"):
  Abstract: A myriad of IoT devices such as bulbs, switches, speakers in a smart home environment allow users to easily control the physical world around them and facilitate their living styles. However, an attacker inside or near a smart home environment can potentially exploit the innate wireless medium used by these devices to exfiltrate sensitive information about the users and their activities, invading user privacy. With this in mind, in this work, we introduce a novel multi-stage privacy attack against user privacy in a smart environment. It is realized utilizing state-of-the-art machine-learning approaches for detecting and identifying particular types of IoT devices, their actions, states, and ongoing user activities in a cascading style by only observing passively the wireless traffic from smart home devices. The attack effectively works on both encrypted and unencrypted communications. We evaluate the efficiency of the attack with real measurements from an extensive set of popular off-the-shelf smart home IoT devices utilizing a set of diverse network protocols like WiFi, ZigBee, and BLE. Our results show that an adversary passively sniffing the network traffic can achieve very high accuracy (above 90%) in identifying the state and actions of targeted smart home devices and their users. In contrast to earlier straightforward approaches, our multi-stage privacy attack can perform activity detection and identification automatically without extensive background knowledge or specifications of the analyzed protocols. This allows an adversary to efficiently aggregate extensive behavior profiles of targeted users. To protect against this privacy leakage, we also propose a countermeasure based on generating spoofed network traffic to hide the real activities of the devices. We also demonstrate that the provided solution provides better protection than existing solutions.

Includes members:
  Member "sensitive information tracking in commodity iot":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time analysis of privacy un aware iot applications")
  Member "a survey on sensor based threats to internet of things iot devices and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time analysis of privacy un aware iot applications")
  Member "iotsense behavioral fingerprinting of iot devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "informing the design of privacy empowering tools for the connected home")
  Member "detection of unauthorized iot devices using machine learning techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "iot device identification using deep learning")
  Member "6thsense a context aware sensor based attack detector for smart devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time analysis of privacy un aware iot applications")
  Member "spying on the smart home privacy attacks and defenses on encrypted iot traffic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors")

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)