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: "n baiot network based detection of iot botnet attacks using deep autoencoders"
  Abstract: The proliferation of IoT devices that can be more easily compromised than desktop computers has led to an increase in IoT-based botnet attacks. To mitigate this threat, there is a need for new methods that detect attacks launched from compromised IoT devices and that differentiate between hours- and milliseconds-long IoT-based attacks. In this article, we propose a novel network-based anomaly detection method for the IoT called N-BaIoT that extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two widely known IoT-based botnets, Mirai and BASHLITE. The evaluation results demonstrated our proposed methods ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices that were part of a botnet.

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"
    - "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"
    - "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 "sereniot collaborative network security policy management and enforcement for smart homes"):
  with members:
    - "sok blockchain technology and its potential use cases"
    - "ciota collaborative iot anomaly detection via blockchain"
    - "blockchains consensus protocols in the wild"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "idiot securing the internet of things like it s 1994"

Connected through hyperedge HE-3 (induced by source paper "making ai forget you data deletion in machine learning"):
  with members:
    - "quantized compressive k means"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "algorithms that remember model inversion attacks and data protection law"
    - "in datacenter performance analysis of a tensor processing unit"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "fixed point quantization of deep convolutional networks"
    - "training deep neural networks with low precision multiplications"
    - "distributed k means and k median clustering on general topologies"

Connected through hyperedge HE-4 (induced by source paper "anomalous communications detection in iot networks using sparse autoencoders"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "distributed anomaly detection using autoencoder neural networks in wsn for iot"
    - "detection of unauthorized iot devices using machine learning techniques"
    - "iot sentinel demo automated device type identification for security enforcement in iot"

Connected through hyperedge HE-5 (induced by source paper "nfv based iot security for home networks using mud"):
  with members:
    - "machine learning ddos detection for consumer internet of things devices"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "peer to peer communication across network address translators"

Connected through hyperedge HE-6 (induced by source paper "subspace methods that are resistant to a limited number of features corrupted by an adversary"):
  with members:
    - "towards evaluating the robustness of neural networks"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "edima early detection of iot malware network activity using machine learning techniques"):
  with members:
    - "early detection of mirai like iot bots in large scale networks through sub sampled packet traffic analysis"

Connected through hyperedge HE-8 (induced by source paper "real time nonparametric anomaly detection in high dimensional settings"):
  with members:
    - "secure distributed dynamic state estimation in wide area smart grids"
    - "real time detection of hybrid and stealthy cyber attacks in smart grid"
    - "multisensor sequential change detection with unknown change propagation pattern"
    - "online cyber attack detection in smart grid a reinforcement learning approach"
    - "anomaly detection with score functions based on nearest neighbor graphs"
    - "a kernel method for the two sample problem"

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)