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: "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
  Abstract: The increasing popularity of specialized Internet-connected devices and appliances, dubbed the Internet-of-Things (IoT), promises both new conveniences and new privacy concerns. Unlike traditional web browsers, many IoT devices have always-on sensors that constantly monitor fine-grained details of users' physical environments and influence the devices' network communications. Passive network observers, such as Internet service providers, could potentially analyze IoT network traffic to infer sensitive details about users. Here, we examine four IoT smart home devices (a Sense sleep monitor, a Nest Cam Indoor security camera, a WeMo switch, and an Amazon Echo) and find that their network traffic rates can reveal potentially sensitive user interactions even when the traffic is encrypted. These results indicate that a technological solution is needed to protect IoT device owner privacy, and that IoT-specific concerns must be considered in the ongoing policy debate around ISP data collection and usage.

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"
    - "peek a boo i see your smart home activities even encrypted"
    - "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"
    - "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 "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-3 (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"
    - "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-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "how secure is your iot network"):
  with members:
    - "peek a boo i see your smart home activities even encrypted"
    - "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"
    - "snap a general purpose network analysis and graph mining library"
    - "network attack detection at flow level"

Connected through hyperedge HE-6 (induced by source paper "privacy leakage in smart homes and its mitigation ifttt as a case study"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "a multiversion programming inspired approach to detecting audio adversarial examples"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving detection of iot devices connected behind a nat in a smart home setup"):
  with members:
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "no nat d user left behind fingerprinting users behind nat from netflow records alone"

Connected through hyperedge HE-8 (induced by source paper "privacy of dependent users against statistical matching"):
  with members:
    - "asymptotic limits of privacy in bayesian time series matching"
    - "a concentration of measure approach to database de anonymization"
    - "single server single message online private information retrieval with side information"
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "fundamental limits of database alignment"
    - "community detection with side information exact recovery under the stochastic block model"
    - "matching graphs with community structure a concentration of measure approach"
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"

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)