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: "real time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns"
  Abstract: Precision farming robots, which target to reduce the amount of herbicides that need to be brought out in the fields, must have the ability to identify crops and weeds in real time to trigger weeding actions. In this paper, we address the problem of CNN-based semantic segmentation of crop fields separating sugar beet plants, weeds, and background solely based on RGB data. We propose a CNN that exploits existing vegetation indexes and provides a classification in real time. Furthermore, it can be effectively re-trained to so far unseen fields with a comparably small amount of training data. We implemented and thoroughly evaluated our system on a real agricultural robot operating in different fields in Germany and Switzerland. The results show that our system generalizes well, can operate at around 20Hz, and is suitable for online operation in the fields.

Connected through hyperedge HE-1 (induced by source paper "rgpnet a real time general purpose semantic segmentation"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "fast scnn fast semantic segmentation network"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "person search by multi scale matching"

Connected through hyperedge HE-2 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "convolutional crfs for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"

Connected through hyperedge HE-3 (induced by source paper "casting geometric constraints in semantic segmentation as semi supervised learning"):
  with members:
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "open3d a modern library for 3d data processing"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "digging into self supervised monocular depth estimation"

Connected through hyperedge HE-4 (induced by source paper "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "gff gated fully fusion for semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "long term visual localization using semantically segmented images"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "in defense of classical image processing fast depth completion on the cpu"

Connected through hyperedge HE-5 (induced by source paper "active learning with weak supervision for cost effective panicle detection in cereal crops"):
  with members:
    - "deep learning in agriculture a survey"
    - "active learning for deep object detection"
    - "cost effective active learning for deep image classification"
    - "deep bayesian active learning with image data"
    - "mask r cnn"
    - "training object class detectors with click supervision"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "s4 net geometry consistent semi supervised semantic segmentation"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "adversarial learning for semi supervised semantic segmentation"
    - "digging into self supervised monocular depth estimation"
    - "good semi supervised learning that requires a bad gan"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"

Connected through hyperedge HE-7 (induced by source paper "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "focal loss for dense object detection"
    - "icnet for real time semantic segmentation on high resolution images"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

Connected through hyperedge HE-8 (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"
    - "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"

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)