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: "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.

Connected through hyperedge HE-1 (induced by source paper "iot behavioral monitoring via network traffic analysis"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "iot device fingerprint using deep learning"
    - "cleartext data transmissions in consumer iot medical devices"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "idiot securing the internet of things like it s 1994"
    - "amplification and drdos attack defense a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors"):
  with members:
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "position tracking for virtual reality using commodity wifi"
    - "a study of mac address randomization in mobile devices and when it fails"
    - "wi fi gesture recognition on existing devices"

Connected through hyperedge HE-3 (induced by source paper "iot device identification using deep learning"):
  with members:
    - "memory forensic analysis of mqtt devices"
    - "detection of unauthorized iot devices using machine learning techniques"

Connected through hyperedge HE-4 (induced by source paper "your smart home can t keep a secret towards automated fingerprinting of iot traffic with neural networks"):
  with members:
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "automated website fingerprinting through deep learning"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "iotscanner detecting and classifying privacy threats in iot neighborhoods"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-5 (induced by source paper "real time analysis of privacy un aware iot applications"):
  with members:
    - "a context aware framework for detecting sensor based threats on smart devices"
    - "aegis a context aware security framework for smart home systems"
    - "program analysis of commodity iot applications for security and privacy challenges and opportunities"
    - "soteria automated iot safety and security analysis"
    - "sensitive information tracking in commodity iot"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"

Connected through hyperedge HE-6 (induced by source paper "pfirewall semantics aware customizable data flow control for home automation systems"):
  with members:
    - "cross app interference threats in smart homes categorization detection and handling"
    - "soteria automated iot safety and security analysis"
    - "sensitive information tracking in commodity iot"
    - "a developer friendly library for smart home iot privacy preserving traffic obfuscation"
    - "user perceptions of smart home iot privacy"
    - "iotsan fortifying the safety of iot systems"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"

Connected through hyperedge HE-7 (induced by source paper "multi user multi device aware access control system for smart home"):
  with members:
    - "a context aware framework for detecting sensor based threats on smart devices"
    - "a system level behavioral detection framework for compromised cps devices smart grid case"
    - "healthguard a machine learning based security framework for smart healthcare systems"
    - "aegis a context aware security framework for smart home systems"
    - "soteria automated iot safety and security analysis"
    - "sensitive information tracking in commodity iot"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"

Connected through hyperedge HE-8 (induced by source paper "pingpong packet level signatures for smart home device events"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "keeping the smart home private with smart er iot traffic shaping"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"
    - "k fingerprinting a robust scalable website fingerprinting technique"

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)