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 plant plant identification with convolutional neural networks"
  Abstract: This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England. To gain intuition on the chosen features from the CNN model (opposed to a 'black box' solution), a visualisation technique based on the deconvolutional networks (DN) is utilized. It is found that venations of different order have been chosen to uniquely represent each of the plant species. Experimental results using these CNN features with different classifiers show consistency and superiority compared to the state-of-the art solutions which rely on hand-crafted features.

Connected through hyperedge HE-1 (induced by source paper "modern cnns for iot based farms"):
  with members:
    - "deep learning in agriculture a survey"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "deep learning in agriculture a survey"):
  with members:
    - "deep recurrent neural networks for mapping winter vegetation quality coverage via multi temporal sar sentinel 1"
    - "an analysis of deep neural network models for practical applications"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "crop planning using stochastic visual optimization"
    - "using deep learning for image based plant disease detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep learning in neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "deepweeds a multiclass weed species image dataset for deep learning"):
  with members:
    - "squeeze and excitation networks"
    - "crafting gbd net for object detection"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "state of the art and gaps for deep learning on limited training data in remote sensing"):
  with members:
    - "generative adversarial networks and probabilistic graph models for hyperspectral image classification"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "image to image translation with conditional adversarial networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "large scale plant classification with deep neural networks"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep networks with stochastic depth"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "theano new features and speed improvements"

Connected through hyperedge HE-6 (induced by source paper "automatic model based dataset generation for fast and accurate crop and weeds detection"):
  with members:
    - "fast robust monocular depth estimation for obstacle detection with fully convolutional networks"
    - "playing for data ground truth from computer games"
    - "semantic instance annotation of street scenes by 3d to 2d label transfer"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "how transferable are features in deep neural networks"

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)