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: "consistent optimization for single shot object detection"
  Abstract: We present consistent optimization for single stage object detection. Previous works of single stage object detectors usually rely on the regular, dense sampled anchors to generate hypothesis for the optimization of the model. Through an examination of the behavior of the detector, we observe that the misalignment between the optimization target and inference configurations has hindered the performance improvement. We propose to bride this gap by consistent optimization, which is an extension of the traditional single stage detector's optimization strategy. Consistent optimization focuses on matching the training hypotheses and the inference quality by utilizing of the refined anchors during training. To evaluate its effectiveness, we conduct various design choices based on the state-of-the-art RetinaNet detector. We demonstrate it is the consistent optimization, not the architecture design, that yields the performance boosts. Consistent optimization is nearly cost-free, and achieves stable performance gains independent of the model capacities or input scales. Specifically, utilizing consistent optimization improves RetinaNet from 39.1 AP to 40.1 AP on COCO dataset without any bells or whistles, which surpasses the accuracy of all existing state-of-the-art one-stage detectors when adopting ResNet-101 as backbone. The code will be made available.

Connected through hyperedge HE-1 (induced by source paper "reppoints point set representation for object detection"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "metaanchor learning to detect objects with customized anchors"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "refinedetlite a lightweight one stage object detection framework for cpu only devices"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "searching for mobilenetv3"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bounding box regression with uncertainty for accurate object detection"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "foveabox beyond anchor based object detector"):
  with members:
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "cornernet detecting objects as paired keypoints"
    - "autofocus efficient multi scale inference"
    - "metaanchor learning to detect objects with customized anchors"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "deep feature pyramid reconfiguration for object detection"

Connected through hyperedge HE-4 (induced by source paper "centerface joint face detection and alignment using face as point"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "retinaface single stage dense face localisation in the wild"
    - "lffd a light and fast face detector for edge devices"
    - "objects as points"
    - "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"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

Connected through hyperedge HE-6 (induced by source paper "stela a real time scene text detector with learned anchor"):
  with members:
    - "region proposal by guided anchoring"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "scene text detection with supervised pyramid context network"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text 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)