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: "deepproposal hunting objects by cascading deep convolutional layers"
  Abstract: In this paper we evaluate the quality of the activation layers of a convolutional neural network (CNN) for the gen- eration of object proposals. We generate hypotheses in a sliding-window fashion over different activation layers and show that the final convolutional layers can find the object of interest with high recall but poor localization due to the coarseness of the feature maps. Instead, the first layers of the network can better localize the object of interest but with a reduced recall. Based on this observation we design a method for proposing object locations that is based on CNN features and that combines the best of both worlds. We build an inverse cascade that, going from the final to the initial convolutional layers of the CNN, selects the most promising object locations and refines their boxes in a coarse-to-fine manner. The method is efficient, because i) it uses the same features extracted for detection, ii) it aggregates features using integral images, and iii) it avoids a dense evaluation of the proposals due to the inverse coarse-to-fine cascade. The method is also accurate; it outperforms most of the previously proposed object proposals approaches and when plugged into a CNN-based detector produces state-of-the- art detection performance.

Connected through hyperedge HE-1 (induced by source paper "cascade retinanet maintaining consistency for single stage object detection"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "consistent optimization for single shot object detection"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "scale aware trident networks for object detection"
    - "cornernet detecting objects as paired keypoints"
    - "fa rpn floating region proposals for face detection"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "region proposal by guided anchoring"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "metaanchor learning to detect objects with customized anchors"
    - "single shot bidirectional pyramid networks for high quality object detection"
    - "toward scale invariance and position sensitive region proposal networks"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "cascade r cnn delving into high quality object detection"

Connected through hyperedge HE-3 (induced by source paper "ipg net image pyramid guidance network for object detection"):
  with members:
    - "segmentation is all you need"
    - "centernet keypoint triplets for object detection"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "detnet a backbone network for object detection"
    - "deep feature pyramid reconfiguration for object detection"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "harvesting visual objects from internet images via deep learning based objectness assessment"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "deepqoe a unified framework for learning to predict video qoe"
    - "instance level salient object segmentation"
    - "exploiting web images for dataset construction a domain robust approach"
    - "quasi homography warps in image stitching"
    - "instance sensitive fully convolutional networks"
    - "convolutional oriented boundaries"
    - "salient object subitizing"

Connected through hyperedge HE-5 (induced by source paper "fpan fine grained and progressive attention localization network for data retrieval"):
  with members:
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "dssd deconvolutional single shot detector"
    - "dual deep network for visual tracking"
    - "ensemble of part detectors for simultaneous classification and localization"
    - "stnet selective tuning of convolutional networks for object localization"
    - "progressive attention networks for visual attribute prediction"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-6 (induced by source paper "customized image narrative generation via interactive visual question generation and answering"):
  with members:
    - "visual question answering datasets algorithms and future challenges"
    - "the color of the cat is gray 1 million full sentences visual question answering fsvqa"
    - "analyzing the behavior of visual question answering models"
    - "hierarchical question image co attention for visual question answering"
    - "generating natural questions about an image"
    - "exploring models and data for image question answering"
    - "vqa visual question answering"
    - "densecap fully convolutional localization networks for dense captioning"

Connected through hyperedge HE-7 (induced by source paper "cascade region proposal and global context for deep object detection"):
  with members:
    - "deformable convolutional networks"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "what makes for effective detection proposals"

Connected through hyperedge HE-8 (induced by source paper "scalenet guiding object proposal generation in supermarkets and beyond"):
  with members:
    - "unrealcv connecting computer vision to unreal engine"
    - "researchdoom and cocodoom learning computer vision with games"
    - "instance sensitive fully convolutional networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "scale aware pixelwise object proposal networks"
    - "ssd single shot multibox detector"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"

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)