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: "anchor box optimization for object detection"
  Abstract: In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. However, these frameworks usually pre-define anchor box shapes in heuristic ways and fix the sizes during training. To improve the accuracy and reduce the effort of designing anchor boxes, we propose to dynamically learn the anchor shapes, which allows the anchors to automatically adapt to the data distribution and the network learning capability. The learning approach can be easily implemented with stochastic gradient descent and can be plugged into any anchor box-based detection framework. The extra training cost is almost negligible and it has no impact on the inference time or memory cost. Exhaustive experiments demonstrate that the proposed anchor optimization method consistently achieves significant improvement ($\ge 1\%$ mAP absolute gain) over the baseline methods on several benchmark datasets including Pascal VOC 07+12, MS COCO and Brainwash. Meanwhile, the robustness is also verified towards different anchor initialization methods and the number of anchor shapes, which greatly simplifies the problem of anchor box design.

Connected through hyperedge HE-1 (induced by source paper "mixture model based bounding box density estimation for object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "region proposal by guided anchoring"
    - "revisiting feature alignment for one stage object detection"
    - "towards accurate one stage object detection with ap loss"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"

Connected through hyperedge HE-2 (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"
    - "consistent optimization 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"

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"
    - "consistent optimization 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"

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)