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: "filtered channel features for pedestrian detection"
  Abstract: This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. #R##N#Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.

Connected through hyperedge HE-1 (induced by source paper "adapted center and scale prediction more stable and more accurate"):
  with members:
    - "psc net learning part spatial co occurence for occluded pedestrian detection"
    - "semantic head enhanced pedestrian detection in a crowd"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "foveabox beyond anchor based object detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"

Connected through hyperedge HE-2 (induced by source paper "virtual to real adaptation of pedestrian detectors for smart cities"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "yolov3 an incremental improvement"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "citypersons a diverse dataset for pedestrian detection"
    - "training constrained deconvolutional networks for road scene semantic segmentation"
    - "mot16 a benchmark for multi object tracking"
    - "how far are we from solving pedestrian detection"
    - "a unified multi scale deep convolutional neural network for fast object detection"

Connected through hyperedge HE-3 (induced by source paper "fmt fusing multi task convolutional neural network for person search"):
  with members:
    - "neural person search machines"
    - "end to end comparative attention networks for person re identification"
    - "svdnet for pedestrian retrieval"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "joint detection and identification feature learning for person search"
    - "learning to rank in person re identification with metric ensembles"
    - "deep feature learning with relative distance comparison for person re identification"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "eurocity persons a novel benchmark for person detection in traffic scenes"):
  with members:
    - "yolov3 an incremental improvement"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "soft nms improving object detection with one line of code"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"
    - "learning non maximum suppression"
    - "perceptual generative adversarial networks for small object detection"

Connected through hyperedge HE-5 (induced by source paper "attribute aware pedestrian detection in a crowd"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "acquisition of localization confidence for accurate object detection"
    - "iterative crowd counting"

Connected through hyperedge HE-6 (induced by source paper "adaptive nms refining pedestrian detection in a crowd"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "acquisition of localization confidence for accurate object detection"
    - "iterative crowd counting"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "soft nms improving object detection with one line of code"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"

Connected through hyperedge HE-7 (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"
    - "learning multilayer channel features for pedestrian detection"

Connected through hyperedge HE-8 (induced by source paper "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "unbiasing semantic segmentation for robot perception using synthetic data feature transfer"
    - "cornernet detecting objects as paired keypoints"
    - "multispectral pedestrian detection via simultaneous detection and segmentation"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "focal loss for dense object detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"

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)