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 blended representation and deep learning for generic object detection"):
  Abstract: In this paper, we propose a novel object detection algorithm named "Deep Regionlets" by integrating deep neural networks and conventional detection schema for accurate generic object detection. Motivated by the advantages of regionlets on modeling object deformation and multiple aspect ratios, we incorporate regionlets into an end-to-end trainable deep learning framework. The deep regionlets framework consists of a region selection network and a deep regionlet learning module. Specifically, given a detection bounding box proposal, the region selection network provides guidance on where to select regions from which features can be learned from. The regionlet learning module focuses on local feature selection and transformation to alleviate the effects of appearance variations. To this end, we first realize non-rectangular region selection within the detection framework to accommodate variations in object appearance. Moreover, we design a "gating network" within the regionlet leaning module to enable soft regionlet selection and pooling. The Deep Regionlets framework is trained end-to-end without additional efforts. We present the results of ablation studies and extensive experiments on PASCAL VOC and Microsoft COCO datasets. The proposed algorithm outperforms state-of-the-art algorithms, such as RetinaNet and Mask R-CNN, even without additional segmentation labels.

Includes members:
  Member "an analysis of pre training on object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural data server a large scale search engine for transfer learning data")
  Member "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "penet object detection using points estimation in aerial images")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "crystal loss and quality pooling for unconstrained face verification and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems")
  Member "a fast and accurate system for face detection identification and verification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalizing face representation with unlabeled data")
  Member "deep feature pyramid reconfiguration for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection")
  Member "multi scale location aware kernel representation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "semantic graph convolutional networks for 3d human pose regression")

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)