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: "crafting gbd net for object detection"
  Abstract: The visual cues from multiple support regions of different sizes and resolutions are complementary in classifying a candidate box in object detection. Effective integration of local and contextual visual cues from these regions has become a fundamental problem in object detection. #R##N#In this paper, we propose a gated bi-directional CNN (GBD-Net) to pass messages among features from different support regions during both feature learning and feature extraction. Such message passing can be implemented through convolution between neighboring support regions in two directions and can be conducted in various layers. Therefore, local and contextual visual patterns can validate the existence of each other by learning their nonlinear relationships and their close interactions are modeled in a more complex way. It is also shown that message passing is not always helpful but dependent on individual samples. Gated functions are therefore needed to control message transmission, whose on-or-offs are controlled by extra visual evidence from the input sample. The effectiveness of GBD-Net is shown through experiments on three object detection datasets, ImageNet, Pascal VOC2007 and Microsoft COCO. This paper also shows the details of our approach in wining the ImageNet object detection challenge of 2016, with source code provided on \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "1st place solutions for openimage2019 object detection and instance segmentation"):
  with members:
    - "revisiting the sibling head in object detector"
    - "detecting 11k classes large scale object detection without fine grained bounding boxes"
    - "double head rcnn rethinking classification and localization for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "cornernet detecting objects as paired keypoints"
    - "gradient harmonized single stage detector"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-3 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-4 (induced by source paper "libra r cnn towards balanced learning for object detection"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "deep feature pyramid reconfiguration for object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "augfpn improving multi scale feature learning for object detection"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "ccnet criss cross attention for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep learning for generic object detection a survey"
    - "monocular depth estimation using multi scale continuous crfs as sequential deep networks"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"

Connected through hyperedge HE-7 (induced by source paper "hicore visual hierarchical context reasoning"):
  with members:
    - "connection sensitive attention u net for accurate retinal vessel segmentation"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "graph neural networks a review of methods and applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "iterative visual reasoning beyond convolutions"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-8 (induced by source paper "assd attentive single shot multibox detector"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "learn to pay attention"
    - "fssd feature fusion single shot multibox detector"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "relation networks for object detection"
    - "dssd deconvolutional single shot detector"
    - "single shot refinement neural network for object detection"

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)