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: "deep learning in agriculture a survey"
  Abstract: Abstract   Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Connected through hyperedge HE-1 (induced by source paper "road network and travel time extraction from multiple look angles with spacenet data"):
  with members:
    - "city scale road extraction from satellite imagery v2 road speeds and travel times"
    - "spacenet mvoi a multi view overhead imagery dataset"
    - "spacenet a remote sensing dataset and challenge series"
    - "from satellite imagery to disaster insights"
    - "benchmark dataset for automatic damaged building detection from post hurricane remotely sensed imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a large dataset of object scans"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "driver drowsiness detection model using convolutional neural networks techniques for android application"):
  with members:
    - "real time driver drowsiness detection for android application using deep neural networks techniques"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "fsd feature skyscraper detector for stem end and blossom end of navel orange"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "convolutional neural networks with alternately updated clique"
    - "deep learning for generic object detection a survey"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "fssd feature fusion single shot multibox detector"
    - "self normalizing neural networks"

Connected through hyperedge HE-4 (induced by source paper "grape detection segmentation and tracking using deep neural networks and three dimensional association"):
  with members:
    - "monocular camera based fruit counting and mapping with semantic data association"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "deep learning for generic object detection a survey"
    - "datasheets for datasets"
    - "agricultural robotics the future of robotic agriculture"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "mask r cnn"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "modern cnns for iot based farms"):
  with members:
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "towards principled design of deep convolutional networks introducing simpnet"
    - "an analysis of deep neural network models for practical applications"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "using deep learning for image based plant disease detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "deep fruit detection in orchards"

Connected through hyperedge HE-6 (induced by source paper "multi vision attention networks for on line red jujube grading"):
  with members:
    - "on the convergence of adam and beyond"
    - "convolutional neural networks with alternately updated clique"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "dual skipping networks"
    - "learning transferable architectures for scalable image recognition"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeeze and excitation networks"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-7 (induced by source paper "nemesyst a hybrid parallelism deep learning based framework applied for internet of things enabled food retailing refrigeration systems"):
  with members:
    - "capsule routing via variational bayes"
    - "on the convergence of adam and beyond"
    - "using deep learning to predict plant growth and yield in greenhouse environments"
    - "towards a deep unified framework for nuclear reactor perturbation analysis"
    - "deep bayesian self training"
    - "dynamic routing between capsules"
    - "recent trends in deep learning based natural language processing"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-8 (induced by source paper "fruit detection segmentation and 3d visualisation of environments in apple orchards"):
  with members:
    - "single pixel reconstruction for one stage instance segmentation"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "deep fruit detection in orchards"

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.LG(Machine Learning)