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: "no nat d user left behind fingerprinting users behind nat from netflow records alone"
  Abstract: It is generally recognized that the traffic generated by an individual connected to a network acts as his biometric signature. Several tools exploit this fact to fingerprint and monitor users. Often, though, these tools assume to access the entire traffic, including IP addresses and payloads. This is not feasible on the grounds that both performance and privacy would be negatively affected. In reality, most ISPs convert user traffic into NetFlow records for a concise representation that does not include, for instance, any payloads. More importantly, large and distributed networks are usually NAT'd, thus a few IP addresses may be associated to thousands of users. We devised a new fingerprinting framework that overcomes these hurdles. Our system is able to analyze a huge amount of network traffic represented as NetFlows, with the intent to track people. It does so by accurately inferring when users are connected to the network and which IP addresses they are using, even though thousands of users are hidden behind NAT. Our prototype implementation was deployed and tested within an existing large metropolitan WiFi network serving about 200,000 users, with an average load of more than 1,000 users simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned out to be very effective, with an accuracy greater than 90%. We also devised new tools and refined existing ones that may be applied to other contexts related to NetFlow analysis.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving detection of iot devices connected behind a nat in a smart home setup"):
  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"

Connected through hyperedge HE-3 (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"
    - "closing the blinds four strategies for protecting smart home privacy from network observers"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "toward an efficient website fingerprinting defense"
    - "website detection using remote traffic analysis"

Connected through hyperedge HE-4 (induced by source paper "the dark side channel of mobile devices a survey on network traffic analysis"):
  with members:
    - "robust smartphone app identification via encrypted network traffic analysis"
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "signature generation for sensitive information leakage in android applications"

Connected through hyperedge HE-5 (induced by source paper "profiling users by modeling web transactions"):
  with members:
    - "iot sentinel demo automated device type identification for security enforcement in iot"

Connected through hyperedge HE-6 (induced by source paper "antmonitor system and applications"):
  with members:
    - "demo recon revealing and controlling pii leaks in mobile network traffic"
    - "haystack a multi purpose mobile vantage point in user space"

Connected through hyperedge HE-7 (induced by source paper "antmonitor a system for on device mobile network monitoring and its applications"):
  with members:
    - "demo recon revealing and controlling pii leaks in mobile network traffic"

Connected through hyperedge HE-8 (induced by source paper "no place to hide that bytes won t reveal sniffing location based encrypted traffic to track a user s position"):
  with members:
    - "can t you hear me knocking identification of user actions on android apps via traffic analysis"
    - "geo indistinguishability differential privacy for location based systems"

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)