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 "gated multi layer convolutional feature extraction network for robust pedestrian detection"):
  Abstract: Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large variant on sizes and with occlusions remains a challenging problem. In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions. The proposed gated feature extraction framework consists of squeeze units, gate units and a concatenation layer which perform feature dimension squeezing, feature elements manipulation and convolutional features combination from multiple CNN layers, respectively. We proposed two different gate models which can manipulate the regional feature maps in a channel-wise selection manner and a spatial-wise selection manner, respectively. Experiments on the challenging CityPersons dataset demonstrate the effectiveness of the proposed method, especially on detecting those small-size and occluded pedestrians.

Includes members:
  Member "sam rcnn scale aware multi resolution multi channel pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coupled network for robust pedestrian detection with gated multi layer feature extraction and deformable occlusion handling")
  Member "occlusion aware r cnn detecting pedestrians in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "citypersons a diverse dataset for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "what can help pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "repulsion loss detecting pedestrians in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "learning multilayer channel features for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "synthtext3d synthesizing scene text images from 3d virtual worlds")
  Member "how far are we from solving pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "pedestrian detection the elephant in the room")

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)