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: "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
  Abstract: The growing market for smart home IoT devices promises new conveniences for consumers while presenting new challenges for preserving privacy within the home. Many smart home devices have always-on sensors that capture users' offline activities in their living spaces and transmit information about these activities on the Internet. In this paper, we demonstrate that an ISP or other network observer can infer privacy sensitive in-home activities by analyzing Internet traffic from smart homes containing commercially-available IoT devices even when the devices use encryption. We evaluate several strategies for mitigating the privacy risks associated with smart home device traffic, including blocking, tunneling, and rate-shaping. Our experiments show that traffic shaping can effectively and practically mitigate many privacy risks associated with smart home IoT devices. We find that 40KB/s extra bandwidth usage is enough to protect user activities from a passive network adversary. This bandwidth cost is well within the Internet speed limits and data caps for many smart homes.

Connected through hyperedge HE-1 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors"):
  with members:
    - "peek a boo i see your smart home activities even encrypted"
    - "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-2 (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"
    - "peek a boo i see your smart home activities even encrypted"
    - "automated website fingerprinting through deep learning"
    - "a smart home is no castle privacy vulnerabilities of 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-3 (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"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"

Connected through hyperedge HE-4 (induced by source paper "security of the internet of things vulnerabilities attacks and countermeasures"):
  with members:
    - "security implications of fog computing on the internet of things"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "understanding iot security through the data crystal ball where we are now and where we are going to be"
    - "survey of security and privacy issues of internet of things"
    - "trail topology authentication in rpl"
    - "a survey of attacks security mechanisms and challenges in wireless sensor networks"
    - "security in wireless sensor networks issues and challenges"

Connected through hyperedge HE-5 (induced by source paper "the house that knows you user authentication based on iot data"):
  with members:
    - "soteria automated iot safety and security analysis"
    - "is feature selection secure against training data poisoning"
    - "certified defenses for data poisoning attacks"
    - "i know why you went to the clinic risks and realization of https traffic analysis"
    - "a comparative usability study of two factor authentication"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-6 (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"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"
    - "k fingerprinting a robust scalable website fingerprinting technique"

Connected through hyperedge HE-7 (induced by source paper "how secure is your iot network"):
  with members:
    - "peek a boo i see your smart home activities even encrypted"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "snap a general purpose network analysis and graph mining library"
    - "network attack detection at flow level"

Connected through hyperedge HE-8 (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"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"

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)