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: "kitsune an ensemble of autoencoders for online network intrusion detection"
  Abstract: Neural networks have become an increasingly popular solution for network intrusion detection systems (NIDS). Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. However, a drawback of neural networks is the amount of resources needed to train them. Many network gateways and routers devices, which could potentially host an NIDS, simply do not have the memory or processing power to train and sometimes even execute such models. More importantly, the existing neural network solutions are trained in a supervised manner. Meaning that an expert must label the network traffic and update the model manually from time to time. #R##N#In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner. Kitsune's core algorithm (KitNET) uses an ensemble of neural networks called autoencoders to collectively differentiate between normal and abnormal traffic patterns. KitNET is supported by a feature extraction framework which efficiently tracks the patterns of every network channel. Our evaluations show that Kitsune can detect various attacks with a performance comparable to offline anomaly detectors, even on a Raspberry PI. This demonstrates that Kitsune can be a practical and economic NIDS.

Connected through hyperedge HE-1 (induced by source paper "text a 3 activation anomaly analysis"):
  with members:
    - "dla dense layer analysis for adversarial example detection"
    - "deep learning for anomaly detection a survey"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "testing deep neural networks"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-2 (induced by source paper "sparseids learning packet sampling with reinforcement learning"):
  with members:
    - "explainability and adversarial robustness for rnns"
    - "neural speed reading via skim rnn"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "learning to skim text"

Connected through hyperedge HE-3 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"
    - "learning to evade static pe machine learning malware models via reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "rallying adversarial techniques against deep learning for network security"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "adversarial attacks and defences competition"
    - "adversarial vision challenge"
    - "adversarial spheres"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "hu fu hardware and software collaborative attack framework against neural networks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-6 (induced by source paper "deep variational semi supervised novelty detection"):
  with members:
    - "deep semi supervised anomaly detection"
    - "deep learning for anomaly detection a survey"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep anomaly detection using geometric transformations"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "learning sampling distributions for robot motion planning"
    - "safer classification by synthesis"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-7 (induced by source paper "robust anomaly detection and backdoor attack detection via differential privacy"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "preserving differential privacy in adversarial learning with provable robustness"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "machine learning models that remember too much"
    - "preserving statistical validity in adaptive data analysis"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-8 (induced by source paper "efficient cyber attacks detection in industrial control systems using lightweight neural networks"):
  with members:
    - "real time evasion attacks with physical constraints on deep learning based anomaly detectors in industrial control systems"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "cyberattack detection using deep generative models with variational inference"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "stealthy deception attacks against scada 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)