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: "how far are we from solving pedestrian detection"
  Abstract: Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector. Our results characterize both localization and background-versus-foreground errors. To address localization errors we study the impact of training annotation noise on the detector performance, and show that we can improve even with a small portion of sanitized training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech dataset, and provide a new sanitized set of training and test annotations.

Connected through hyperedge HE-1 (induced by source paper "pedestrian detection the elephant in the room"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "mask guided attention network for occluded pedestrian detection"
    - "deep high resolution representation learning for visual recognition"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"

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"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "driving in the matrix can virtual worlds replace human generated annotations for real world tasks"

Connected through hyperedge HE-3 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-4 (induced by source paper "high level semantic feature detection a new perspective for pedestrian detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep feature pyramid reconfiguration for object detection"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-5 (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-6 (induced by source paper "pmc gans generating multi scale high quality pedestrian with multimodal cascaded gans"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "learning to generate synthetic data via compositing"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "image super resolution using very deep residual channel attention networks"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "synthetic data augmentation using gan for improved liver lesion classification"

Connected through hyperedge HE-7 (induced by source paper "empirical upper bound in object detection and more"):
  with members:
    - "an analysis of pre training on object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "efficientdet scalable and efficient object detection"
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "contextual relabelling of detected objects"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"

Connected through hyperedge HE-8 (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"

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)