human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "iou balanced loss functions for single stage object detection"):
  Abstract: Single-stage detectors are efficient. However, we find that the loss functions adopted by single-stage detectors are sub-optimal for accurate localization. The standard cross entropy loss for classification is independent of localization task and drives all the positive examples to learn as high classification score as possible regardless of localization accuracy during training. As a result, there will be detections that have high classification score but low IoU or low classification score but high IoU. And the detections with low classification score but high IOU will be suppressed by the ones with high classification score but low IOU during NMS, hurting the localization accuracy. For the standard smooth L1 loss, the gradient is dominated by the outliers that have poorly localization accuracy and this is harmful for accurate localization. In this work, we propose IoU-balanced loss functions that consist of IoU-balanced classification loss and IoU-balanced localization loss to solve the above problems. The IoU-balanced classification loss focuses more attention on positive examples with high IOU and can enhance the correlation between classification and localization task. The IoU-balanced localization loss decreases the gradient of the examples with low IoU and increases the gradient of examples with high IoU, which can improve the localization accuracy of models. Sufficient studies on MS COCO demonstrate that both IoU-balanced classification loss and IoU-balanced localization loss can bring substantial improvement for the single-stage detectors. Without whistles and bells, the proposed methods can improve AP by 1.1% for single-stage detectors and the improvement for AP at higher IoU threshold is especially large, such as 2.3% for AP90. The source code will be made available.

Includes members:
  Member "libra r cnn towards balanced learning for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "mmdetection open mmlab detection toolbox and benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mixpath a unified approach for one shot neural architecture search")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proposal learning for semi supervised object detection")
  Member "prime sample attention in object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning from noisy anchors for one stage object detection")
  Member "gradient harmonized single stage detector":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting the sibling head in object detector")
  Member "acquisition of localization confidence for accurate object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "revisiting the sibling head in object detector")
  Member "soft nms improving object detection with one line of code":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting the sibling head in object detector")
  Member "improving object localization with fitness nms and bounded iou loss":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cpm r cnn calibrating point guided misalignment in object detection")

Question: Please tell me which class the source paper 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)