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: "machine learning ddos detection for consumer internet of things devices"
  Abstract: An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g., limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.

Connected through hyperedge HE-1 (induced by source paper "iot behavioral monitoring via network traffic analysis"):
  with members:
    - "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"
    - "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 "automating botnet detection with graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "pytorch an imperative style high performance deep learning library"
    - "graph neural networks a review of methods and applications"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "neural message passing for quantum chemistry"
    - "learning to represent programs with graphs"
    - "semi supervised classification with graph convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "machine learning in iot security current solutions and future challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "ai based two stage intrusion detection for software defined iot networks"
    - "machine intelligence techniques for next generation context aware wireless networks"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "ten ai stepping stones for cybersecurity"):
  with members:
    - "adversarial deep learning for robust detection of binary encoded malware"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "provable limitations of deep learning"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "a first look at deep learning apps on smartphones"
    - "prada protecting against dnn model stealing attacks"
    - "logan membership inference attacks against generative models"
    - "malicious url detection using machine learning a survey"

Connected through hyperedge HE-5 (induced by source paper "peek inside the closed world evaluating autoencoder based detection of ddos to cloud"):
  with members:
    - "anomaly detection using autoencoders in high performance computing systems"
    - "one class convolutional neural network"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "anomaly detection using one class neural networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "adam a method for stochastic optimization"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-6 (induced by source paper "pingpong packet level signatures for smart home device events"):
  with members:
    - "peek a boo i see your smart home activities even encrypted"
    - "keeping the smart home private with smart er iot traffic shaping"
    - "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"
    - "k fingerprinting a robust scalable website fingerprinting technique"

Connected through hyperedge HE-7 (induced by source paper "anomalous communications detection in iot networks using sparse autoencoders"):
  with members:
    - "distributed anomaly detection using autoencoder neural networks in wsn for iot"
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "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-8 (induced by source paper "nfv based iot security for home networks using mud"):
  with members:
    - "n baiot network based detection of iot botnet attacks using deep autoencoders"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "peer to peer communication across network address translators"

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)