human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "iot behavioral monitoring via network traffic analysis"):
  Abstract: Smart homes, enterprises, and cities are increasingly being equipped with a plethora of Internet of Things (IoT), ranging from smart-lights to security cameras. While IoT networks have the potential to benefit our lives, they create privacy and security challenges not seen with traditional IT networks. Due to the lack of visibility, operators of such smart environments are not often aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pattern of IoTs, automate IoT classification, deduce their operating context, and detect anomalous behavior indicative of cyber-attacks. #R##N#We begin this thesis by surveying IoT ecosystem, while reviewing current approaches to vulnerability assessments, intrusion detection, and behavioral monitoring. For our first contribution, we collect traffic traces and characterize the network behavior of IoT devices via attributes from traffic patterns. We develop a robust machine learning-based inference engine trained with these attributes and demonstrate real-time classification of 28 IoT devices with over 99% accuracy. Our second contribution enhances the classification by reducing the cost of attribute extraction while also identifying IoT device states. Prototype implementation and evaluation demonstrate the ability of our supervised machine learning method to detect behavioral changes for five IoT devices. Our third and final contribution develops a modularized unsupervised inference engine that dynamically accommodates the addition of new IoT devices and/or updates to existing ones, without requiring system-wide retraining of the model. We demonstrate via experiments that our model can automatically detect attacks and firmware changes in ten IoT devices with over 94% accuracy.

Includes members:
  Member "machine learning ddos detection for consumer internet of things devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "automating botnet detection with graph neural networks")
  Member "peek a boo i see your smart home activities even encrypted":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "et tu alexa when commodity wifi devices turn into adversarial motion sensors")
  Member "n baiot network based detection of iot botnet attacks using deep autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sereniot collaborative network security policy management and enforcement for smart homes")
  Member "iot device fingerprint using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer learning using cnn for handwritten devanagari character recognition")
  Member "cleartext data transmissions in consumer iot medical devices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "characterizing smart home iot traffic in the wild")
  Member "a smart home is no castle privacy vulnerabilities of encrypted iot traffic":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "smart home personal assistants a security and privacy review")
  Member "iot sentinel demo automated device type identification for security enforcement in iot":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "informing the design of privacy empowering tools for the connected home")
  Member "idiot securing the internet of things like it s 1994":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sereniot collaborative network security policy management and enforcement for smart homes")

Question: Please tell me which class the source paper 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.NI(Networking and Internet Architecture)