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 security techniques based on machine learning"
  Abstract: Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine learning based IoT authentication, access control, secure offloading and malware detection schemes to protect data privacy. In this article, we discuss the challenges that need to be addressed to implement these machine learning based security schemes in practical IoT systems.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning in flipit"):
  with members:
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for cyber security"):
  with members:
    - "deep reinforcement learning for detecting malicious websites"
    - "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"
    - "multi agent deep reinforcement learning with human strategies"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "relational deep reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"

Connected through hyperedge HE-3 (induced by source paper "a survey of machine and deep learning methods for internet of things iot security"):
  with members:
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "machine learning ddos detection for consumer internet of things devices"
    - "lifelong learning for sentiment classification"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "detection of unauthorized iot devices using machine learning techniques"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deep learning for secure mobile edge computing"

Connected through hyperedge HE-4 (induced by source paper "when deep learning meets security"):
  with members:
    - "artificial intelligence enabled software defined networking a comprehensive overview"
    - "early stage malware prediction using recurrent neural networks"
    - "detecting malicious powershell commands using deep neural networks"
    - "deep neural networks for bot detection"
    - "henet a deep learning approach on intel circledr processor trace for effective exploit detection"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "the rise of social bots"

Connected through hyperedge HE-5 (induced by source paper "self organized low power iot networks a distributed learning approach"):
  with members:
    - "optimized resource provisioning and operation control for low power wide area iot networks"
    - "grant free radio access iot networks scalability analysis in coexistence scenarios"
    - "low power wide area network analysis can lora scale"
    - "energy efficient scheduling and grouping for machine type communications over cellular networks"
    - "the best of both worlds stochastic and adversarial bandits"

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)