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 "ai based pilgrim detection using convolutional neural networks"):
  Abstract: Pilgrimage represents the most important Islamic religious gathering in the world where millions of pilgrims visit the holy places of Makkah and Madinah to perform their rituals. The safety and security of pilgrims is the highest priority for the authorities. In Makkah, 5000 cameras are spread around the holy for monitoring pilgrims, but it is almost impossible to track all events by humans considering the huge number of images collected every second. To address this issue, we propose to use artificial intelligence technique based on deep learning and convolution neural networks to detect and identify Pilgrims and their features. For this purpose, we built a comprehensive dataset for the detection of pilgrims and their genders. Then, we develop two convolutional neural networks based on YOLOv3 and Faster-RCNN for the detection of Pilgrims. Experiments results show that Faster RCNN with Inception v2 feature extractor provides the best mean average precision over all classes of 51%.

Includes members:
  Member "activity monitoring of islamic prayer salat postures using deep learning":
    also connected through other hyperedges:
      (none)
  Member "aerial images processing for car detection using convolutional neural networks comparison between faster r cnn and yolov3":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "activity monitoring of islamic prayer salat postures using deep learning")
  Member "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "activity monitoring of islamic prayer salat postures using deep learning")
  Member "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks")
  Member "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fusionlane multi sensor fusion for lane marking semantic segmentation using deep neural networks")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relation network for multi label aerial image classification")
  Member "car detection using unmanned aerial vehicles comparison between faster r cnn and yolov3":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "activity monitoring of islamic prayer salat postures using deep learning")

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)