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: "imbalance problems in object detection a review"
  Abstract: In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonomy. Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature. In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before. Moreover, in order to keep our review up to date, we provide an accompanying webpage which catalogs papers addressing imbalance problems, according to our problem-based taxonomy. Researchers can track newer studies on this webpage available at: this https URL .

Connected through hyperedge HE-1 (induced by source paper "learning from multiple experts self paced knowledge distillation for long tailed classification"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "learning to reweight examples for robust deep learning"
    - "evolutionary data measures understanding the difficulty of text classification tasks"
    - "dynamic few shot visual learning without forgetting"
    - "automated curriculum learning for neural networks"

Connected through hyperedge HE-2 (induced by source paper "iou uniform r cnn breaking through the limitations of rpn"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "prime sample attention in object detection"
    - "cornernet detecting objects as paired keypoints"
    - "bounding box regression with uncertainty for accurate object detection"

Connected through hyperedge HE-3 (induced by source paper "are sampling heuristics necessary in object detectors"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "prime sample attention in object detection"

Connected through hyperedge HE-4 (induced by source paper "generating positive bounding boxes for balanced training of object detectors"):
  with members:
    - "libra r cnn towards balanced learning for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "prime sample attention in object detection"
    - "deep feature pyramid reconfiguration for object detection"
    - "path aggregation network for instance segmentation"
    - "localization recall precision lrp a new performance metric for object detection"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "towards good practices for instance segmentation"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "towards good practices for video object segmentation"
    - "mask r cnn"
    - "coco stuff thing and stuff classes in context"
    - "deep residual learning for image recognition"

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)