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: "taking a deeper look at pedestrians"
  Abstract: In this paper we study the use of convolutional neural networks (convnets) for the task of pedestrian detection. Despite their recent diverse successes, convnets historically underperform compared to other pedestrian detectors. We deliberately omit explicitly modelling the problem into the network (e.g. parts or occlusion modelling) and show that we can reach competitive performance without bells and whistles. In a wide range of experiments we analyse small and big convnets, their architectural choices, parameters, and the influence of different training data, including pre-training on surrogate tasks. #R##N#We present the best convnet detectors on the Caltech and KITTI dataset. On Caltech our convnets reach top performance both for the Caltech1x and Caltech10x training setup. Using additional data at training time our strongest convnet model is competitive even to detectors that use additional data (optical flow) at test time.

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 "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-3 (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-4 (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-5 (induced by source paper "widerperson a diverse dataset for dense pedestrian detection in the wild"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "weakly aligned cross modal learning for multispectral pedestrian detection"):
  with members:
    - "box level segmentation supervised deep neural networks for accurate and real time multispectral pedestrian detection"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "multispectral pedestrian detection via simultaneous detection and segmentation"
    - "illumination aware faster r cnn for robust multispectral pedestrian detection"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"

Connected through hyperedge HE-7 (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"

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"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "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"

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)