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: "understanding iot security through the data crystal ball where we are now and where we are going to be"
  Abstract: Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.

Connected through hyperedge HE-1 (induced by source paper "security of the internet of things vulnerabilities attacks and countermeasures"):
  with members:
    - "security implications of fog computing on the internet of things"
    - "a survey on sensor based threats to internet of things iot devices and applications"
    - "spying on the smart home privacy attacks and defenses on encrypted iot traffic"
    - "survey of security and privacy issues of internet of things"
    - "trail topology authentication in rpl"
    - "a survey of attacks security mechanisms and challenges in wireless sensor networks"
    - "security in wireless sensor networks issues and challenges"

Connected through hyperedge HE-2 (induced by source paper "physical integrity attack detection of surveillance camera with deep learning based video frame interpolation"):
  with members:
    - "a study on smart online frame forging attacks against video surveillance system"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-3 (induced by source paper "hdmi walk attacking hdmi distribution networks via consumer electronic control protocol"):
  with members:
    - "sensitive information tracking in commodity iot"
    - "advertising in the iot era vision and challenges"
    - "iotdots a digital forensics framework for smart environments"
    - "6thsense a context aware sensor based attack detector for smart devices"

Connected through hyperedge HE-4 (induced by source paper "program analysis of commodity iot applications for security and privacy challenges and opportunities"):
  with members:
    - "cross app interference threats in smart homes categorization detection and handling"
    - "soteria automated iot safety and security analysis"
    - "incentivized delivery network of iot software updates based on trustless proof of distribution"
    - "peek a boo i see your smart home activities even encrypted"
    - "sensitive information tracking in commodity iot"
    - "iotdots a digital forensics framework for smart environments"
    - "iotsan fortifying the safety of iot systems"
    - "internet of things security research a rehash of old ideas or new intellectual challenges"

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)