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: "r fcn 3000 at 30fps decoupling detection and classification"
  Abstract: We present R-FCN-3000, a large-scale real-time object detector in which objectness detection and classification are decoupled. To obtain the detection score for an RoI, we multiply the objectness score with the fine-grained classification score. Our approach is a modification of the R-FCN architecture in which position-sensitive filters are shared across different object classes for performing localization. For fine-grained classification, these position-sensitive filters are not needed. R-FCN-3000 obtains an mAP of 34.9% on the ImageNet detection dataset and outperforms YOLO-9000 by 18% while processing 30 images per second. We also show that the objectness learned by R-FCN-3000 generalizes to novel classes and the performance increases with the number of training object classes - supporting the hypothesis that it is possible to learn a universal objectness detector. Code will be made available.

Connected through hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "gabor convolutional networks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"

Connected through hyperedge HE-2 (induced by source paper "cs r fcn cross supervised learning for large scale object detection"):
  with members:
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "yolo9000 better faster stronger"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "lsda large scale detection through adaptation"

Connected through hyperedge HE-3 (induced by source paper "an analysis of pre training on object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "cornernet detecting objects as paired keypoints"
    - "do better imagenet models transfer better"
    - "taskonomy disentangling task transfer learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-4 (induced by source paper "exploring object relation in mean teacher for cross domain detection"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "semantic foggy scene understanding with synthetic data"
    - "yolov3 an incremental improvement"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "exploring visual relationship for image captioning"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-5 (induced by source paper "learning from noisy anchors for one stage object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "scaling object detection by transferring classification weights"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "batch normalization is a cause of adversarial vulnerability"
    - "few shot object detection via feature reweighting"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "exploring the limits of weakly supervised pretraining"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "zero annotation object detection with web knowledge transfer"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-7 (induced by source paper "detecting 11k classes large scale object detection without fine grained bounding boxes"):
  with members:
    - "yolov3 an incremental improvement"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "sniper efficient multi scale training"
    - "soft sampling for robust object detection"
    - "exploiting web images for weakly supervised object detection"
    - "soft nms improving object detection with one line of code"
    - "zero annotation object detection with web knowledge transfer"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-8 (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"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "evolving boxes for fast vehicle detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

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)