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 "deep regionlets for object detection"):
  Abstract: A key challenge in generic object detection is being to handle large variations in object scale, poses, viewpoints, especially part deformations when determining the location for specified object categories. Recent advances in deep neural networks have achieved promising results for object detection by extending the traditional detection methodologies using the convolutional neural network architectures. In this paper, we make an attempt to incorporate another traditional detection schema, Regionlet into an end-to-end trained deep learning framework, and perform ablation studies on its behavior on multiple object detection datasets. More specifically, we propose a "region selection network" and a "gating network". The region selection network serves as a guidance on where to select regions to learn the features from. Additionally, the gating network serves as a local feature selection module to select and transform feature maps to be suitable for detection task. It acts as soft Regionlet selection and pooling. The proposed network is trained end-to-end without additional efforts. Extensive experiments and analysis on the PASCAL VOC dataset and Microsoft COCO dataset show that the proposed framework achieves comparable state-of-the-art results.

Includes members:
  Member "cross domain visual recognition via domain adaptive dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning simple thresholded features with sparse support recovery")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "multi scale location aware kernel representation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection")
  Member "pseudo mask augmented object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic graph convolutional networks for 3d human pose regression")
  Member "revisiting rcnn on awakening the classification power of faster rcnn":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting the sibling head in object detector")
  Member "soft sampling for robust object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object detection as a positive unlabeled problem")
  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 "deformable part based fully convolutional network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "block bilinear superdiagonal fusion for visual question answering and visual relationship 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)