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 "d iot a federated self learning anomaly detection system for iot"):
  Abstract: IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise. This has led to a new category of malware specifically targeting IoT devices. However, existing intrusion detection techniques are not effective in detecting compromised IoT devices given the massive scale of the problem in terms of the number of different types of devices and manufacturers involved. In this paper, we present D\"IoT, an autonomous self-learning distributed system for detecting compromised IoT devices effectively. In contrast to prior work, D\"IoT uses a novel self-learning approach to classify devices into device types and build normal communication profiles for each of these that can subsequently be used to detect anomalous deviations in communication patterns. D\"IoT utilizes a federated learning approach for aggregating behavior profiles efficiently. To the best of our knowledge, it is the first system to employ a federated learning approach to anomaly-detection-based intrusion detection. Consequently, D\"IoT can cope with emerging new and unknown attacks. We systematically and extensively evaluated more than 30 off-the-shelf IoT devices over a long term and show that D\"IoT is highly effective (95.6% detection rate) and fast (~257 ms) at detecting devices compromised by, for instance, the infamous Mirai malware. D\"IoT reported no false alarms when evaluated in a real-world smart home deployment setting.

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 "iot behavioral monitoring via network traffic analysis")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "dolphinattack inaudible voice commands":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems")
  Member "robust physical world attacks on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "iot sentinel demo automated device type identification for security enforcement in iot":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "iot behavioral monitoring via network traffic analysis")
  Member "federated multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "towards evaluating the robustness of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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.CR(Cryptography and Security)