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: "an edge fog cloud platform for anticipatory learning process designed for internet of mobile things"
  Abstract: This paper presents a novel architecture for data analytics targeting an anticipatory learning process in the context of the Internet of Mobile Things. The architecture is geo-distributed and composed by edge, fog, and cloud resources that operate collectively to support such an anticipatory learning process. We designed the architecture to manage large volumes of data streams coming from the IoMT devices, analyze in successive phases climbing up in the hierarchy of resources from edge, fog and cloud. We discuss the characteristics of the analytical tasks at each layer. We notice that the amount of data being transported in the network decreases going from the edge, to the fog and finally to the cloud, while the complexity of the computation increases. Such design allows to support different kind of analytical needs, from real-time to historical according to the type of resource being utilized. We have implemented the proposed architecture as a proof-of-concept using the transit data feeds from the area of Greater Moncton, Canada.

Connected through hyperedge HE-1 (induced by source paper "decentralized smart surveillance through microservices platform"):
  with members:
    - "a study on smart online frame forging attacks against video surveillance system"
    - "blendmas a blockchain enabled decentralized microservices architecture for smart public safety"
    - "fog computing a taxonomy survey and future directions"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "an exploration of blockchain enabled decentralized capability based access control strategy for space situation awareness"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "smart surveillance as an edge network service from harr cascade svm to a lightweight cnn"
    - "constructing trustworthy and safe communities on a blockchain enabled social credits system"

Connected through hyperedge HE-2 (induced by source paper "smart surveillance as an edge network service from harr cascade svm to a lightweight cnn"):
  with members:
    - "feature fused ssd fast detection for small objects"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "towards lightweight convolutional neural networks for object detection"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"):
  with members:
    - "smart surveillance as an edge network service from harr cascade svm to a lightweight cnn"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"
    - "correlation filters with limited boundaries"

Connected through hyperedge HE-4 (induced by source paper "real time index authentication for event oriented surveillance video query using blockchain"):
  with members:
    - "fog computing a taxonomy survey and future directions"
    - "smart surveillance as an edge network service from harr cascade svm to a lightweight cnn"
    - "smart contract based access control for the internet of things"
    - "real time human detection as an edge service enabled by a lightweight cnn"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "fully convolutional siamese networks for object tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "high speed tracking with kernelized correlation filters"

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.CY(Computers and Society)