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: "adaptive nms refining pedestrian detection in a crowd"
  Abstract: Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density; (2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors; and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks.

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"
    - "mask guided attention network for occluded pedestrian detection"
    - "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"
    - "mask guided attention network for occluded pedestrian detection"
    - "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 "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"
    - "mask guided attention network for occluded pedestrian detection"
    - "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-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"
    - "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"
    - "crowdhuman a benchmark for detecting human 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"
    - "mask guided attention network for occluded pedestrian detection"
    - "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 "distance iou loss faster and better learning for bounding box regression"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "object detection based on region decomposition and assembly"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "data augmentation for object detection via progressive and selective instance switching"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-7 (induced by source paper "double anchor r cnn for human detection in a crowd"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "soft nms improving object detection with one line of code"
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "citypersons a diverse dataset for pedestrian detection"
    - "cascade r cnn delving into high quality object detection"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-8 (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"
    - "mask guided attention network for occluded pedestrian detection"
    - "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)