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: "iot sentinel demo automated device type identification for security enforcement in iot"
  Abstract: With the rapid growth of the Internet-of-Things (IoT), concerns about the security of IoT devices have become prominent. Several vendors are producing IP-connected devices for home and small office networks that often suffer from flawed security designs and implementations. They also tend to lack mechanisms for firmware updates or patches that can help eliminate security vulnerabilities. Securing networks where the presence of such vulnerable devices is given, requires a brownfield approach: applying necessary protection measures within the network so that potentially vulnerable devices can coexist without endangering the security of other devices in the same network. In this paper, we present IoT Sentinel, a system capable of automatically identifying the types of devices being connected to an IoT network and enabling enforcement of rules for constraining the communications of vulnerable devices so as to minimize damage resulting from their compromise. We show that IoT Sentinel is effective in identifying device types and has minimal performance overhead.

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"
    - "a smart home is no castle privacy vulnerabilities of encrypted iot traffic"
    - "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 "informing the design of privacy empowering tools for the connected home"):
  with members:
    - "iot inspector crowdsourcing labeled network traffic from smart home devices at scale"
    - "pingpong packet level signatures for smart home device events"
    - "discovering smart home internet of things privacy norms using contextual integrity"
    - "user perceptions of smart home iot privacy"
    - "iotsense behavioral fingerprinting of iot devices"

Connected through hyperedge HE-3 (induced by source paper "sereniot collaborative network security policy management and enforcement for smart homes"):
  with members:
    - "sok blockchain technology and its potential use cases"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "ciota collaborative iot anomaly detection via blockchain"
    - "blockchains consensus protocols in the wild"
    - "idiot securing the internet of things like it s 1994"

Connected through hyperedge HE-4 (induced by source paper "iot network behavioral fingerprint inference with limited network trace for cyber investigation a meta learning approach"):
  with members:
    - "one class convolutional neural network"
    - "prototypical networks for few shot learning"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "text understanding from scratch"

Connected through hyperedge HE-5 (induced by source paper "deep complex networks for protocol agnostic radio frequency device fingerprinting in the wild"):
  with members:
    - "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms"
    - "gated complex recurrent neural networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "complex gated recurrent neural networks"
    - "an introduction to deep learning for the physical layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deep complex networks"
    - "image augmentation using radial transform for training deep neural networks"

Connected through hyperedge HE-6 (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"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-7 (induced by source paper "iot network security from the perspective of adversarial deep learning"):
  with members:
    - "generative adversarial networks for black box api attacks with limited training data"
    - "spectrum data poisoning with adversarial deep learning"
    - "active deep learning attacks under strict rate limitations for online api calls"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "generative adversarial learning for spectrum sensing"
    - "deep learning for launching and mitigating wireless jamming attacks"
    - "adversarial attacks on deep learning based radio signal classification"
    - "deep learning for iot big data and streaming analytics a survey"

Connected through hyperedge HE-8 (induced by source paper "iot inspector crowdsourcing labeled network traffic from smart home devices at scale"):
  with members:
    - "improving heart rate variability measurements from consumer smartwatches with machine learning"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "playing with danger a taxonomy and evaluation of threats to smart toys"
    - "keeping the smart home private with smart er iot traffic shaping"
    - "security and privacy analyses of internet of things children s toys"
    - "cleartext data transmissions in consumer iot medical devices"

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)