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: "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
  Abstract: Reducing the use of agrochemicals is an important component toward sustainable agriculture. Robots that can perform targeted weed control offer the potential to contribute to this goal, for example, through specialized weeding actions such as selective spraying or mechanical weed removal. A prerequisite of such systems is a reliable and robust plant classification system that is able to distinguish crop and weed in the field. A major challenge in this context is the fact that different fields show a large variability. Thus, classification systems have to robustly cope with substantial environmental changes with respect to weed pressure and weed types, growth stages of the crop, visual appearance, and soil conditions. In this letter, we propose a novel crop-weed classification system that relies on a fully convolutional network with an encoder-decoder structure and incorporates spatial information by considering image sequences. Exploiting the crop arrangement information that is observable from the image sequences enables our system to robustly estimate a pixel-wise labeling of the images into crop and weed, i.e., a semantic segmentation. We provide a thorough experimental evaluation, which shows that our system generalizes well to previously unseen fields under varying environmental conditions-a key capability to actually use such systems in precision framing. We provide comparisons to other state-of-the-art approaches and show that our system substantially improves the accuracy of crop-weed classification without requiring a retraining of the model.

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"
    - "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"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"

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:
    - "deep learning in agriculture a survey"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "automatic model based dataset generation for fast and accurate crop and weeds detection"
    - "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"

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)