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 "rethinking classification and localization for object detection"):
  Abstract: Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the weight matrix in the fc-head and find that it learns spatial sensitive transformations. We believe that this allows fc-head to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.

Includes members:
  Member "centernet keypoint triplets for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector")
  Member "towards adversarially robust object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  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 "scale aware trident networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting the sibling head in object detector")
  Member "objects as points":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "cornernet lite efficient keypoint based object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder")
  Member "bottom up object detection by grouping extreme and center points":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "rethinking classification and localization for cascade r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "r3det refined single stage detector with feature refinement for rotating object")

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)