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 "hybrid channel based pedestrian detection"):
  Abstract: Pedestrian detection has achieved great improvements with the help of Convolutional Neural Networks (CNNs). CNN can learn high-level features from input images, but the insufficient spatial resolution of CNN feature channels (feature maps) may cause a loss of information, which is harmful especially to small instances. In this paper, we propose a new pedestrian detection framework, which extends the successful RPN+BF framework to combine handcrafted features and CNN features. RoI-pooling is used to extract features from both handcrafted channels (e.g. HOG+LUV, CheckerBoards or RotatedFilters) and CNN channels. Since handcrafted channels always have higher spatial resolution than CNN channels, we apply RoI-pooling with larger output resolution to handcrafted channels to keep more detailed information. Our ablation experiments show that the developed handcrafted features can reach better detection accuracy than the CNN features extracted from the VGG-16 net, and a performance gain can be achieved by combining them. Experimental results on Caltech pedestrian dataset with the original annotations and the improved annotations demonstrate the effectiveness of the proposed approach. When using a more advanced RPN in our framework, our approach can be further improved and get competitive results on both benchmarks.

Includes members:
  Member "pcn part and context information for pedestrian detection with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "baod budget aware object detection")
  Member "small scale pedestrian detection based on somatic topology localization and temporal feature aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ssa cnn semantic self attention cnn for pedestrian detection")
  Member "manifold adversarial learning":
    also connected through other hyperedges:
      (none)
  Member "fused deep neural networks for efficient pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ssa cnn semantic self attention cnn for pedestrian detection")
  Member "exploring multi branch and high level semantic networks for improving pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monocular pedestrian orientation estimation based on deep 2d 3d feedforward")
  Member "illuminating pedestrians via simultaneous detection segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pedestrian detection the elephant in the room")
  Member "what can help pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  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")

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)