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: "sniper efficient multi scale training"
  Abstract: We present SNIPER, an algorithm for performing efficient multi-scale training in instance level visual recognition tasks. Instead of processing every pixel in an image pyramid, SNIPER processes context regions around ground-truth instances (referred to as chips) at the appropriate scale. For background sampling, these context-regions are generated using proposals extracted from a region proposal network trained with a short learning schedule. Hence, the number of chips generated per image during training adaptively changes based on the scene complexity. SNIPER only processes 30% more pixels compared to the commonly used single scale training at 800x1333 pixels on the COCO dataset. But, it also observes samples from extreme resolutions of the image pyramid, like 1400x2000 pixels. As SNIPER operates on resampled low resolution chips (512x512 pixels), it can have a batch size as large as 20 on a single GPU even with a ResNet-101 backbone. Therefore it can benefit from batch-normalization during training without the need for synchronizing batch-normalization statistics across GPUs. SNIPER brings training of instance level recognition tasks like object detection closer to the protocol for image classification and suggests that the commonly accepted guideline that it is important to train on high resolution images for instance level visual recognition tasks might not be correct. Our implementation based on Faster-RCNN with a ResNet-101 backbone obtains an mAP of 47.6% on the COCO dataset for bounding box detection and can process 5 images per second during inference with a single GPU. Code is available at this https URL.

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"
    - "deep adaptive proposal network for object detection in optical remote sensing images"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-2 (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-3 (induced by source paper "rethinking zero shot video classification end to end training for realistic applications"):
  with members:
    - "revisiting training strategies and generalization performance in deep metric learning"
    - "action recognition with spatial temporal discriminative filter banks"
    - "divide and conquer the embedding space for metric learning"
    - "action2vec a crossmodal embedding approach to action learning"
    - "cross and learn cross modal self supervision"
    - "informed democracy voting based novelty detection for action recognition"
    - "visual data synthesis via gan for zero shot video classification"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "in defense of graph inference algorithms for weakly supervised object localization"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "learning to find common objects across few image collections"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "dissimilarity coefficient based weakly supervised object detection"
    - "focal loss for dense object detection"
    - "revisiting knowledge transfer for training object class detectors"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-5 (induced by source paper "a two stream symmetric network with bidirectional ensemble for aerial image matching"):
  with members:
    - "attentive semantic alignment with offset aware correlation kernels"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "end to end weakly supervised semantic alignment"
    - "convolutional neural network architecture for geometric matching"
    - "dctm discrete continuous transformation matching for semantic flow"
    - "squeeze and excitation networks"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"

Connected through hyperedge HE-6 (induced by source paper "joint coco and mapillary workshop at iccv 2019 keypoint detection challenge track technical report distribution aware coordinate representation for human pose estimation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "cascaded pyramid network for multi person pose estimation"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "flowing convnets for human pose estimation in videos"
    - "joint training of a convolutional network and a graphical model for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "objects as points"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-8 (induced by source paper "a feasible framework for arbitrary shaped scene text recognition"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "efficientdet scalable and efficient object detection"
    - "icdar2019 robust reading challenge on arbitrary shaped text rrc art"
    - "fcos fully convolutional one stage object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "fots fast oriented text spotting with a unified network"
    - "yolov3 an incremental improvement"

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)