human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning multilayer channel features for pedestrian detection"
  Abstract: Pedestrian detection based on the combination of convolutional neural network (CNN) and traditional handcrafted features (i.e., HOG+LUV) has achieved great success. In general, HOG+LUV are used to generate the candidate proposals and then CNN classifies these proposals. Despite its success, there is still room for improvement. For example, CNN classifies these proposals by the fully connected layer features, while proposal scores and the features in the inner-layers of CNN are ignored. In this paper, we propose a unifying framework called multi-layer channel features (MCF) to overcome the drawback. It first integrates HOG+LUV with each layer of CNN into a multi-layer image channels. Based on the multi-layer image channels, a multi-stage cascade AdaBoost is then learned. The weak classifiers in each stage of the multi-stage cascade are learned from the image channels of corresponding layer. Experiments on Caltech data set, INRIA data set, ETH data set, TUD-Brussels data set, and KITTI data set are conducted. With more abundant features, an MCF achieves the state of the art on Caltech pedestrian data set (i.e., 10.40% miss rate). Using new and accurate annotations, an MCF achieves 7.98% miss rate. As many non-pedestrian detection windows can be quickly rejected by the first few stages, it accelerates detection speed by 1.43 times. By eliminating the highly overlapped detection windows with lower scores after the first stage, it is 4.07 times faster than negligible performance loss.

Connected through hyperedge HE-1 (induced by source paper "synthtext3d synthesizing scene text images from 3d virtual worlds"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-2 (induced by source paper "hybrid channel based pedestrian detection"):
  with members:
    - "pcn part and context information for pedestrian detection with cnns"
    - "small scale pedestrian detection based on somatic topology localization and temporal feature aggregation"
    - "manifold adversarial learning"
    - "fused deep neural networks for efficient pedestrian detection"
    - "exploring multi branch and high level semantic networks for improving pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "what can help pedestrian detection"
    - "ian the individual aggregation network for person search"

Connected through hyperedge HE-3 (induced by source paper "coupled network for robust pedestrian detection with gated multi layer feature extraction and deformable occlusion handling"):
  with members:
    - "sam rcnn scale aware multi resolution multi channel pedestrian detection"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "couplenet coupling global structure with local parts for object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-4 (induced by source paper "mask guided attention network for occluded pedestrian detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "person search via a mask guided two stream cnn model"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "cascade learning by optimally partitioning"
    - "repulsion loss detecting pedestrians in a crowd"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-5 (induced by source paper "gated multi layer convolutional feature extraction network for robust pedestrian detection"):
  with members:
    - "sam rcnn scale aware multi resolution multi channel pedestrian detection"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "repulsion loss detecting pedestrians in a crowd"
    - "how far are we from solving pedestrian detection"
    - "is faster r cnn doing well for pedestrian detection"

Connected through hyperedge HE-6 (induced by source paper "exploring multi branch and high level semantic networks for improving pedestrian detection"):
  with members:
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "dssd deconvolutional single shot detector"
    - "dilated residual networks"
    - "scale aware face detection"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-7 (induced by source paper "triply supervised decoder networks for joint detection and segmentation"):
  with members:
    - "panoptic segmentation"
    - "panoptic feature pyramid networks"
    - "learning a discriminative feature network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "biseg simultaneous instance segmentation and semantic segmentation with fully convolutional networks"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-8 (induced by source paper "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"):
  with members:
    - "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "inner and inter label propagation salient object detection in the wild"
    - "salient object detection a benchmark"
    - "very deep convolutional networks for large scale image recognition"
    - "learning rich features from rgb d images for object detection and segmentation"

Question: Please tell me which class the center node 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)