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: "ten years of pedestrian detection what have we learned"
  Abstract: Paper-by-paper results make it easy to miss the forest for the trees.We analyse the remarkable progress of the last decade by discussing the main ideas explored in the 40+ detectors currently present in the Caltech pedestrian detection benchmark. We observe that there exist three families of approaches, all currently reaching similar detection quality. Based on our analysis, we study the complementarity of the most promising ideas by combining multiple published strategies. This new decision forest detector achieves the current best known performance on the challenging Caltech-USA dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "the pedestrian patterns dataset"):
  with members:
    - "fast r cnn"
    - "fully convolutional networks for semantic segmentation"

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 "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 "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-6 (induced by source paper "forecasting pedestrian trajectory with machine annotated training data"):
  with members:
    - "human action recognition and prediction a survey"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "exploring the limits of weakly supervised pretraining"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "future person localization in first person videos"

Connected through hyperedge HE-7 (induced by source paper "ssa cnn semantic self attention cnn for 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"
    - "fused deep neural networks for efficient pedestrian detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "citypersons a diverse dataset for pedestrian detection"
    - "squeeze and excitation networks"
    - "repulsion loss detecting pedestrians in a crowd"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-8 (induced by source paper "gfd ssd gated fusion double ssd for multispectral pedestrian detection"):
  with members:
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "fssd feature fusion single shot multibox detector"
    - "focal loss for dense object detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dssd deconvolutional single shot detector"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "is faster r cnn doing well 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)