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: "illuminating pedestrians via simultaneous detection segmentation"
  Abstract: Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion network to enable joint supervision on semantic segmentation and pedestrian detection. When placed properly, the additional supervision helps guide features in shared layers to become more sophisticated and helpful for the downstream pedestrian detector. Using this approach, we find weakly annotated boxes to be sufficient for considerable performance gains. We provide an in-depth analysis to demonstrate how shared layers are shaped by the segmentation supervision. In doing so, we show that the resulting feature maps become more semantically meaningful and robust to shape and occlusion. Overall, our simultaneous detection and segmentation framework achieves a considerable gain over the state-of-the-art on the Caltech pedestrian dataset, competitive performance on KITTI, and executes 2x faster than competitive methods.

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 "dafe fd density aware feature enrichment for face detection"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "single shot text detector with regional attention"

Connected through hyperedge HE-3 (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"
    - "what can help pedestrian detection"
    - "learning multilayer channel features for pedestrian detection"
    - "ian the individual aggregation network for person search"

Connected through hyperedge HE-4 (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"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"

Connected through hyperedge HE-5 (induced by source paper "assisted excitation of activations a learning technique to improve object detectors"):
  with members:
    - "yolov3 an incremental improvement"
    - "triply supervised decoder networks for joint detection and segmentation"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "single shot text detector with regional attention"
    - "single shot object detection with enriched semantics"
    - "mask r cnn"
    - "large age gap face verification by feature injection in deep networks"

Connected through hyperedge HE-6 (induced by source paper "m3d rpn monocular 3d region proposal network for object detection"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "a general pipeline for 3d detection of vehicles"
    - "surfconv bridging 3d and 2d convolution for rgbd images"
    - "deep feature pyramid reconfiguration for object detection"
    - "pedestrian detection with autoregressive network phases"
    - "localization recall precision lrp a new performance metric for object detection"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"

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"
    - "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"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "pedhunter occlusion robust pedestrian detector in crowded scenes"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "repulsion loss detecting pedestrians in a crowd"
    - "how far are we from solving pedestrian detection"
    - "fused dnn a deep neural network fusion approach to fast and robust 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)