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: "scale aware fast r cnn for pedestrian detection"
  Abstract: In this work, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intra-category variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in sub-networks which detect pedestrians with scales from disjoint ranges. Outputs from all the sub-networks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech, INRIA, and ETH, and obtains competitive results on KITTI.

Connected through hyperedge HE-1 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection"):
  with members:
    - "towards partial supervision for generic object counting in natural scenes"
    - "fine grained recognition accounting for subtle differences between similar classes"
    - "3c net category count and center loss for weakly supervised action localization"
    - "mask guided attention network for occluded pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "object counting and instance segmentation with image level supervision"
    - "occlusion aware r cnn detecting pedestrians in a crowd"

Connected through hyperedge HE-2 (induced by source paper "multi level bottom top and top bottom feature fusion for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "learning from synthetic data for crowd counting in the wild"
    - "uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining"
    - "deeply supervised salient object detection with short connections"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "a shape transformation based dataset augmentation framework for pedestrian detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "a kernel theory of modern data augmentation"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "learning to generate long term future via hierarchical prediction"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "citypersons a diverse dataset for pedestrian detection"

Connected through hyperedge HE-6 (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-7 (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"

Connected through hyperedge HE-8 (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"

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)