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: "augmentation for small object detection"
  Abstract: In recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show that the overlap between small ground-truth objects and the predicted anchors is much lower than the expected IoU threshold. We conjecture this is due to two factors; (1) only a few images are containing small objects, and (2) small objects do not appear enough even within each image containing them. We thus propose to oversample those images with small objects and augment each of those images by copy-pasting small objects many times. It allows us to trade off the quality of the detector on large objects with that on small objects. We evaluate different pasting augmentation strategies, and ultimately, we achieve 9.7\% relative improvement on the instance segmentation and 7.1\% on the object detection of small objects, compared to the current state of the art method on MS COCO.

Connected through hyperedge HE-1 (induced by source paper "penet object detection using points estimation in aerial images"):
  with members:
    - "clustered object detection in aerial images"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "objects as points"
    - "vision meets drones a challenge"
    - "cornernet detecting objects as paired keypoints"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "detnet a backbone network for object detection"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-2 (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"
    - "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"
    - "large receptive field networks for high scale image super resolution"

Connected through hyperedge HE-3 (induced by source paper "slot based image augmentation system for object detection"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "modeling visual context is key to augmenting object detection datasets"
    - "an implementation of faster rcnn with study for region sampling"
    - "relation networks for object detection"
    - "an analysis of scale invariance in object detection snip"
    - "deep network flow for multi object tracking"
    - "deep residual learning for image recognition"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "singular points detection with semantic segmentation networks"):
  with members:
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)