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 "weednet dense semantic weed classification using multispectral images and mav for smart farming"):
  Abstract: Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants. In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded Convolutional Neural Network (CNN), Segnet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the Normalized Difference Vegetation Index (NDVI) as a distinguishable feature for automatic ground truth generation. We train 6 models with different numbers of input channels and condition (fine-tune) it to achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested for MAV integration. Dataset used in this paper is released to support the community and future work.

Includes members:
  Member "multiresolution mapping and informative path planning for uav based terrain monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building an aerial ground robotics system for precision farming")
  Member "build your own visual inertial odometry aided cost effective and open source autonomous drone":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uav ugv autonomous cooperation uav assists ugv to climb a cliff by attaching a tether")
  Member "peduncle detection of sweet pepper for autonomous crop harvesting combined color and 3 d information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards active robotic vision in agriculture a deep learning approach to visual servoing in occluded and unstructured protected cropping environments")
  Member "automatic model based dataset generation for fast and accurate crop and weeds detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "building an aerial ground robotics system for precision farming")
  Member "control of a quadrotor with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to adapt class specific features across domains for semantic segmentation")

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)