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: "towards lightweight convolutional neural networks for object detection"
  Abstract: We propose model with larger spatial size of feature maps and evaluate it on object detection task. With the goal to choose the best feature extraction network for our model we compare several popular lightweight networks. After that we conduct a set of experiments with channels reduction algorithms in order to accelerate execution. Our vehicle detection models are accurate, fast and therefore suit for embedded visual applications. With only 1.5 GFLOPs our best model gives 93.39 AP on validation subset of challenging DETRAC dataset. The smallest of our models is the first to achieve real-time inference speed on CPU with reasonable accuracy drop to 91.43 AP.

Connected through hyperedge HE-1 (induced by source paper "distilling object detectors with fine grained feature imitation"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"
    - "what can help pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "thinet a filter level pruning method for deep neural network compression"
    - "coordinating filters for faster deep neural networks"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "incremental network quantization towards lossless cnns with low precision weights"

Connected through hyperedge HE-2 (induced by source paper "multi layer pruning framework for compressing single shot multibox detector"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "ssd single shot multibox detector"
    - "doubly convolutional neural networks"
    - "dsd regularizing deep neural networks with dense sparse dense training flow"

Connected through hyperedge HE-3 (induced by source paper "development of real time adas object detector for deployment on cpu"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improving object detection from scratch via gated feature reuse"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "coordinating filters for faster deep neural networks"
    - "dsod learning deeply supervised object detectors from scratch"
    - "sanet structure aware network for visual tracking"

Connected through hyperedge HE-4 (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"
    - "an edge fog cloud platform for anticipatory learning process designed for internet of mobile things"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "structural and object detection for phosphene images"):
  with members:
    - "panoroom from the sphere to the 3d layout"
    - "single view and multi view depth fusion"
    - "deep learning features at scale for visual place recognition"
    - "fully convolutional instance aware semantic segmentation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "fast r cnn"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-6 (induced by source paper "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "a compact dnn approaching googlenet level accuracy of classification and domain adaptation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "aggregated residual transformations for deep 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.CV(Computer Vision and Pattern Recognition)