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: "eurocity persons a novel benchmark for person detection in traffic scenes"
  Abstract: Big data has had a great share in the success of deep learning in computer vision. Recent works suggest that there is significant further potential to increase object detection performance by utilizing even bigger datasets. In this paper, we introduce the EuroCity Persons dataset, which provides a large number of highly diverse, accurate and detailed annotations of pedestrians, cyclists and other riders in urban traffic scenes. The images for this dataset were collected on-board a moving vehicle in 31 cities of 12 European countries. With over 238,200 person instances manually labeled in over 47,300 images, EuroCity Persons is nearly one order of magnitude larger than datasets used previously for person detection in traffic scenes. The dataset furthermore contains a large number of person orientation annotations (over 211,200). We optimize four state-of-the-art deep learning approaches (Faster R-CNN, R-FCN, SSD and YOLOv3) to serve as baselines for the new object detection benchmark. In experiments with previous datasets we analyze the generalization capabilities of these detectors when trained with the new dataset. We furthermore study the effect of the training set size, the dataset diversity (day- versus night-time, geographical region), the dataset detail (i.e., availability of object orientation information) and the annotation quality on the detector performance. Finally, we analyze error sources and discuss the road ahead.

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"
    - "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"
    - "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 "panda a gigapixel level human centric video dataset"):
  with members:
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "group reidentification with multigrained matching and integration"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "vision meets drones a challenge"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "autofocus efficient multi scale inference"

Connected through hyperedge HE-3 (induced by source paper "pedestrian detection the elephant in the room"):
  with members:
    - "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"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "sviro synthetic vehicle interior rear seat occupancy dataset and benchmark"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "monet unsupervised scene decomposition and representation"
    - "moment matching for multi source domain adaptation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "museum exhibit identification challenge for domain adaptation and beyond"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-5 (induced by source paper "rsnet an improvement for darknet"):
  with members:
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "driving datasets literature review"):
  with members:
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"

Connected through hyperedge HE-7 (induced by source paper "widerperson a diverse dataset for dense pedestrian detection in the wild"):
  with members:
    - "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"
    - "repulsion loss detecting pedestrians in a crowd"

Connected through hyperedge HE-8 (induced by source paper "multiple object forecasting predicting future object locations in diverse environments"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "looking to relations for future trajectory forecast"
    - "online multi object tracking with dual matching attention networks"
    - "forecasting pedestrian trajectory with machine annotated training data"
    - "peeking into the future predicting future person activities and locations in videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

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)