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: "enhancement of ssd by concatenating feature maps for object detection"
  Abstract: We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. The performance of a deep network is known to be improved as the number of feature maps increases. However, it is difficult to improve the performance by simply raising the number of feature maps. In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD. The enhanced performance was obtained by changing the structure close to the classifier network, rather than growing layers close to the input data, e.g., by replacing VGGNet with ResNet. The proposed network is suitable for sharing the weights in the classifier networks, by which property, the training can be faster with better generalization power. For the Pascal VOC 2007 test set trained with VOC 2007 and VOC 2012 training sets, the proposed network with the input size of 300 x 300 achieved 78.5% mAP (mean average precision) at the speed of 35.0 FPS (frame per second), while the network with a 512 x 512 sized input achieved 80.8% mAP at 16.6 FPS using Nvidia Titan X GPU. The proposed network shows state-of-the-art mAP, which is better than those of the conventional SSD, YOLO, Faster-RCNN and RFCN. Also, it is faster than Faster-RCNN and RFCN.

Connected through hyperedge HE-1 (induced by source paper "cornernet lite efficient keypoint based object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-2 (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-3 (induced by source paper "residual bi fusion feature pyramid network for accurate single shot object detection"):
  with members:
    - "making convolutional networks shift invariant again"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "augmentation for small object detection"
    - "cornernet detecting objects as paired keypoints"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "yolov3 an incremental improvement"
    - "cfenet an accurate and efficient single shot object detector for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "dc spp yolo dense connection and spatial pyramid pooling based yolo for object detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "feature fused ssd fast detection for small objects"
    - "extend the shallow part of single shot multibox detector via convolutional neural network"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "evolving boxes for fast vehicle detection"
    - "r fcn 3000 at 30fps decoupling detection and classification"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "a novel automation assisted cervical cancer reading method based on convolutional neural network"):
  with members:
    - "yolov3 an incremental improvement"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "feature pyramid networks for object detection"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "small object detection using context and attention"):
  with members:
    - "dssd deconvolutional single shot detector"
    - "perceptual generative adversarial networks for small object detection"
    - "residual attention network for image classification"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "action recognition using visual attention"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "deep feature pyramid reconfiguration for object detection"):
  with members:
    - "path aggregation network for instance segmentation"
    - "stairnet top down semantic aggregation for accurate one shot detection"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "learning feature pyramids for human pose estimation"
    - "dssd deconvolutional single shot detector"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-8 (induced by source paper "vehicle image generation going well with the surroundings"):
  with members:
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "image restoration by estimating frequency distribution of local patches"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "squeeze and excitation 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)