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: "automatic model based dataset generation for fast and accurate crop and weeds detection"
  Abstract: Selective weeding is one of the key challenges in the field of agriculture robotics. To accomplish this task, a farm robot should be able to accurately detect plants and to distinguish them between crop and weeds. Most of the promising state-of-the-art approaches make use of appearance-based models trained on large annotated datasets. Unfortunately, creating large agricultural datasets with pixel-level annotations is an extremely time consuming task, actually penalizing the usage of data-driven techniques. In this paper, we face this problem by proposing a novel and effective approach that aims to dramatically minimize the human intervention needed to train the detection and classification algorithms. The idea is to procedurally generate large synthetic training datasets randomizing the key features of the target environment (i.e., crop and weed species, type of soil, light conditions). More specifically, by tuning these model parameters, and exploiting a few real-world textures, it is possible to render a large amount of realistic views of an artificial agricultural scenario with no effort. The generated data can be directly used to train the model or to supplement real-world images. We validate the proposed methodology by using as testbed a modern deep learning based image segmentation architecture. We compare the classification results obtained using both real and synthetic images as training data. The reported results confirm the effectiveness and the potentiality of our approach.

Connected through hyperedge HE-1 (induced by source paper "building an aerial ground robotics system for precision farming"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "agricolmap aerial ground collaborative 3d mapping for precision farming"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
    - "build your own visual inertial drone a cost effective and open source autonomous drone"
    - "an informative path planning framework for uav based terrain monitoring"
    - "an effective multi cue positioning system for agricultural robotics"
    - "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming"

Connected through hyperedge HE-2 (induced by source paper "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming"):
  with members:
    - "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
    - "deep learning in agriculture a survey"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"):
  with members:
    - "towards unsupervised weed scouting for agricultural robotics"
    - "real time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "the rosario dataset multisensor data for localization and mapping in agricultural environments"):
  with members:
    - "3d soil compaction mapping through kriging based exploration with a mobile robot"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"

Connected through hyperedge HE-5 (induced by source paper "deep learning with unsupervised data labeling for weeds detection on uav images"):
  with members:
    - "deep learning in agriculture a survey"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "weednet dense semantic weed classification using multispectral images and mav for smart farming"):
  with members:
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"
    - "build your own visual inertial odometry aided cost effective and open source autonomous drone"
    - "peduncle detection of sweet pepper for autonomous crop harvesting combined color and 3 d information"
    - "control of a quadrotor with reinforcement learning"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)