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: "distributed deep learning model for intelligent video surveillance systems with edge computing"
  Abstract: In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.

Connected through hyperedge HE-1 (induced by source paper "durocmien a deep framework for duroc skeleton extraction in constraint environment"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "markerless tracking of user defined features with deep learning"
    - "maskrnn instance level video object segmentation"
    - "rmpe regional multi person pose estimation"
    - "feature pyramid networks for object detection"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "safa a semi asynchronous protocol for fast federated learning with low overhead"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "towards federated learning at scale system design"
    - "a bi layered parallel training architecture for large scale convolutional neural networks"
    - "asynchronous federated optimization"
    - "edge intelligence the confluence of edge computing and artificial intelligence"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "parallel protein community detection in large scale ppi networks based on multi source learning"
    - "federated learning strategies for improving communication efficiency"

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.CV(Computer Vision and Pattern Recognition)