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: "mask guided attention network for occluded pedestrian detection"
  Abstract: Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we empirically demonstrate that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons and Caltech datasets. Our approach sets a new state-of-the-art on both datasets. Our approach obtains an absolute gain of 9.5% in log-average miss rate, compared to the best reported results on the heavily occluded (HO) pedestrian set of CityPersons test set. Further, on the HO pedestrian set of Caltech dataset, our method achieves an absolute gain of 5.0% in log-average miss rate, compared to the best reported results. Code and models are available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "adapted center and scale prediction more stable and more accurate"):
  with members:
    - "psc net learning part spatial co occurence for occluded pedestrian detection"
    - "semantic head enhanced pedestrian detection in a crowd"
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "foveabox beyond anchor based object detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"

Connected through hyperedge HE-2 (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"
    - "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"
    - "deeper insights into graph convolutional networks for semi supervised learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "ps rcnn detecting secondary human instances in a crowd via primary object suppression"):
  with members:
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "multispectral pedestrian detection via simultaneous detection and segmentation"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "soft nms improving object detection with one line of code"
    - "cascade r cnn delving into high quality object detection"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-5 (induced by source paper "learning to separate detecting heavily occluded objects in urban scenes"):
  with members:
    - "double anchor r cnn for human detection in a crowd"
    - "learning a layout transfer network for context aware object detection"
    - "sinet a scale insensitive convolutional neural network for fast vehicle detection"
    - "adaptive nms refining pedestrian detection in a crowd"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "bounding box regression with uncertainty for accurate object detection"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-6 (induced by source paper "semantic head enhanced 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"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "learning non maximum suppression"
    - "repulsion loss detecting pedestrians in a crowd"

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)