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: "improving object detection from scratch via gated feature reuse"
  Abstract: In this paper, we present a simple and parameter-efficient drop-in module for one-stage object detectors like SSD when learning from scratch (i.e., without pre-trained models). We call our module GFR (Gated Feature Reuse), which exhibits two main advantages. First, we introduce a novel gate-controlled prediction strategy enabled by Squeeze-and-Excitation to adaptively enhance or attenuate supervision at different scales based on the input object size. As a result, our model is more effective in detecting diverse sizes of objects. Second, we propose a feature-pyramids structure to squeeze rich spatial and semantic features into a single prediction layer, which strengthens feature representation and reduces the number of parameters to learn. We apply the proposed structure on DSOD and SSD detection frameworks, and evaluate the performance on PASCAL VOC 2007, 2012 and COCO datasets. With fewer model parameters, GFR-DSOD outperforms the baseline DSOD by 1.4%, 1.1%, 1.7% and 0.6%, respectively. GFR-SSD also outperforms the original SSD and SSD with dense prediction by 3.6% and 2.8% on VOC 2007 dataset. Code is available at: this https URL .

Connected through hyperedge HE-1 (induced by source paper "deep affinity net instance segmentation via affinity"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "upsnet a unified panoptic segmentation network"
    - "deeperlab single shot image parser"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "panoptic feature pyramid networks"
    - "cornernet detecting objects as paired keypoints"
    - "instance segmentation by deep coloring"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"

Connected through hyperedge HE-2 (induced by source paper "skynet a hardware efficient method for object detection and tracking on embedded systems"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "skynet a champion model for dac sdc on low power object detection"
    - "single path nas device aware efficient convnet design"
    - "ternary hybrid neural tree networks for highly constrained iot applications"
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "centernet keypoint triplets for object detection"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "path aggregation network for instance segmentation"
    - "scratchdet training single shot object detectors from scratch"

Connected through hyperedge HE-4 (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-5 (induced by source paper "cornernet detecting objects as paired keypoints"):
  with members:
    - "acquisition of localization confidence for accurate object detection"
    - "soft nms improving object detection with one line of code"
    - "couplenet coupling global structure with local parts for object detection"
    - "improving object localization with fitness nms and bounded iou loss"
    - "feature selective networks for object detection"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"
    - "dssd deconvolutional single shot detector"

Connected through hyperedge HE-6 (induced by source paper "revisiting pre training an efficient training method for image classification"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "simple baselines for human pose estimation and tracking"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "exploring the limits of weakly supervised pretraining"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-7 (induced by source paper "development of real time adas object detector for deployment on cpu"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "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"
    - "towards lightweight convolutional neural networks for object detection"
    - "sanet structure aware network for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "a light dual task neural network for haze removal"):
  with members:
    - "gated fusion network for single image dehazing"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "deep voice real time neural text to speech"
    - "an all in one network for dehazing and beyond"
    - "tensorlayer a versatile library for efficient deep learning development"
    - "dehazenet an end to end system for single image haze removal"
    - "predicting people s 3d poses from short sequences"
    - "what players do with the ball a physically constrained interaction modeling"

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)