human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "soft nms improving object detection with one line of code":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting the sibling head in object detector")
  Member "citypersons a diverse dataset for pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "what can help pedestrian detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "psc net learning part spatial co occurence for occluded pedestrian detection")
  Member "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a shape transformation based dataset augmentation framework for pedestrian detection")
  Member "learning non maximum suppression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "featurenms non maximum suppression by learning feature embeddings")
  Member "perceptual generative adversarial networks for small object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "extended feature pyramid network for small object detection")

Question: Please tell me which class the source paper 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)