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: "quantization mimic towards very tiny cnn for object detection"
  Abstract: In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.

Connected through hyperedge HE-1 (induced by source paper "switchable precision neural networks"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "adaptative inference cost with convolutional neural mixture models"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "be your own teacher improve the performance of convolutional neural networks via self distillation"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "searching for accurate binary neural architectures"
    - "auxiliary learning for deep multi task learning"

Connected through hyperedge HE-2 (induced by source paper "bidet an efficient binarized object detector"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "thundernet towards real time generic object detection"
    - "embarrassingly simple binary representation learning"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "importance estimation for neural network pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "efficient neural network compression"

Connected through hyperedge HE-3 (induced by source paper "siamsnn spike based siamese network for energy efficient and real time object tracking"):
  with members:
    - "deep learning in spiking neural networks"
    - "spiking yolo spiking neural network for energy efficient object detection"
    - "target aware deep tracking"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "distractor aware siamese networks for visual object tracking"
    - "fast and efficient information transmission with burst spikes in deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-4 (induced by source paper "effective training of convolutional neural networks with low bitwidth weights and activations"):
  with members:
    - "knowledge adaptation for efficient semantic segmentation"
    - "single path one shot neural architecture search with uniform sampling"
    - "precision highway for ultra low precision quantization"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-5 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform"):
  with members:
    - "seesaw net convolution neural network with uneven group convolution"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "searching for mobilenetv3"
    - "airface lightweight and efficient model for face recognition"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-6 (induced by source paper "training quantized neural networks with the full precision auxiliary module"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "relational knowledge distillation"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "yolov3 an incremental improvement"
    - "proxquant quantized neural networks via proximal operators"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "self supervised knowledge distillation using singular value decomposition"

Connected through hyperedge HE-7 (induced by source paper "mobinet a mobile binary network for image classification"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "rmdl random multimodel deep learning for classification"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "seeing small faces from robust anchor s perspective"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-8 (induced by source paper "object detection with convolutional neural networks"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "vision meets drones a challenge"
    - "deep learning for generic object detection a survey"
    - "mdcn multi scale deep inception convolutional neural networks for efficient object detection"
    - "yolov3 an incremental improvement"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "learning depth from single images with deep neural network embedding focal length"

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)