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: "closing the blinds four strategies for protecting smart home privacy from network observers"
  Abstract: The growing market for smart home IoT devices promises new conveniences for consumers while presenting novel challenges for preserving privacy within the home. Specifically, Internet service providers or neighborhood WiFi eavesdroppers can measure Internet traffic rates from smart home devices and infer consumers' private in-home behaviors. Here we propose four strategies that device manufacturers and third parties can take to protect consumers from side-channel traffic rate privacy threats: 1) blocking traffic, 2) concealing DNS, 3) tunneling traffic, and 4) shaping and injecting traffic. We hope that these strategies, and the implementation nuances we discuss, will provide a foundation for the future development of privacy-sensitive smart homes.

Connected through hyperedge HE-1 (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"
    - "peek a boo i see your smart home activities even encrypted"
    - "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"

Connected through hyperedge HE-2 (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"
    - "peek a boo i see your smart home activities even encrypted"
    - "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"
    - "k fingerprinting a robust scalable website fingerprinting technique"

Connected through hyperedge HE-3 (induced by source paper "network service dependencies in commodity internet of things devices"):
  with members:
    - "detecting compressed cleartext traffic from consumer internet of things devices"
    - "securing heterogeneous iot with intelligent ddos attack behavior learning"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

Connected through hyperedge HE-4 (induced by source paper "smart home survey on security and privacy"):
  with members:
    - "verifiable round robin scheme for smart homes"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

Connected through hyperedge HE-5 (induced by source paper "verifiable round robin scheme for smart homes"):
  with members:
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

Connected through hyperedge HE-6 (induced by source paper "keeping the smart home private with smart er iot traffic shaping"):
  with members:
    - "oblivious dns practical privacy for dns queries"
    - "a developer friendly library for smart home iot privacy preserving traffic obfuscation"
    - "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"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "toward an efficient website fingerprinting defense"
    - "no nat d user left behind fingerprinting users behind nat from netflow records alone"
    - "website detection using remote traffic analysis"

Connected through hyperedge HE-7 (induced by source paper "machine learning ddos detection for consumer internet of things devices"):
  with members:
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "iot sentinel demo automated device type identification for security enforcement in iot"

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)