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: "deep learning based multiple object visual tracking on embedded system for iot and mobile edge computing applications"
  Abstract: Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image processing using Convolutional Neural Netwoks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the on-board camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort to joint algorithm and hardware design of CNNs is needed.

Connected through hyperedge HE-1 (induced by source paper "fpga based accelerators of deep learning networks for learning and classification a review"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "deep learning for sentiment analysis a survey"
    - "hardware for machine learning challenges and opportunities"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "parallel multi channel convolution using general matrix multiplication"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

Connected through hyperedge HE-2 (induced by source paper "edgecnn convolutional neural network classification model with small inputs for edge computing"):
  with members:
    - "searching for mobilenetv3"
    - "mixconv mixed depthwise convolutional kernels"
    - "on lightweight privacy preserving collaborative learning for internet of things objects"
    - "lep cnn a lightweight edge device assisted privacy preserving cnn inference solution for iot"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "facial expression recognition method based on complexity perception classification algorithm"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

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)