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: "how secure is your iot network"
  Abstract: The proliferation of IoT devices in smart homes, hospitals, and enterprise networks is wide-spread and continuing to increase in a superlinear manner. The question is: how can one assess the security of an IoT network in a holistic manner? In this paper, we have explored two dimensions of security assessment– using vulnerability information and attack vectors of IoT devices and their underlying components (compositional security scores) and using SIEM logs captured from the communications and operations of such devices in a network (dynamic activity metrics). These measures are used to evaluate the security of IoT devices and the overall IoT network, demonstrating the effectiveness of attack circuits as practical tools for computing security metrics (exploitability, impact, and risk to confidentiality, integrity, and availability) of the network. We decided to approach threat modeling using attack graphs. To that end, we propose the notion of attack circuits, which are generated from input/output pairs constructed from CVEs using NLP, and an attack graph composed of these circuits. Our system provides insight into possible attack paths an adversary may utilize based on their exploitability, impact, or overall risk. We have performed experiments on IoT networks to demonstrate the efficacy of the proposed techniques.

Connected through hyperedge HE-1 (induced by source paper "towards deep federated defenses against malware in cloud ecosystems"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "deep hyperedges a framework for transductive and inductive learning on hypergraphs"
    - "multi task deep neural networks for natural language understanding"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "hypergraph convolution and hypergraph attention"
    - "what does bert look at an analysis of bert s attention"
    - "federated learning strategies for improving communication efficiency"
    - "hypergraph neural networks"

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)