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: "using deep learning for image based plant disease detection"
  Abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves an accuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. When testing the model on a set of images collected from trusted online sources - i.e. taken under conditions different from the images used for training - the model still achieves an accuracy of 31.4%. While this accuracy is much higher than the one based on random selection (2.6%), a more diverse set of training data is needed to improve the general accuracy. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path towards smartphone-assisted crop disease diagnosis on a massive global scale.

Connected through hyperedge HE-1 (induced by source paper "rectified meta learning from noisy labels for robust image based plant disease diagnosis"):
  with members:
    - "deep self learning from noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "joint optimization framework for learning with noisy labels"
    - "learning from noisy web data with category level supervision"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "learning to learn from noisy labeled data"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "face detection using deep learning an improved faster rcnn approach"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "noscope optimizing neural network queries over video at scale"

Connected through hyperedge HE-3 (induced by source paper "plantdoc a dataset for visual plant disease detection"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ssd single shot multibox detector"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "unsupervised image translation using adversarial networks for improved plant disease recognition"):
  with members:
    - "generative adversarial networks an overview"
    - "nima neural image assessment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "adversarial nets with perceptual losses for text to image synthesis"

Connected through hyperedge HE-5 (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"
    - "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 "a new benchmark for evaluation of cross domain few shot learning"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "meta learning with differentiable convex optimization"
    - "diversity with cooperation ensemble methods for few shot classification"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "on first order meta learning algorithms"
    - "do better imagenet models transfer better"
    - "meta learning for semi supervised few shot classification"

Connected through hyperedge HE-7 (induced by source paper "mavnet an effective semantic segmentation micro network for mav based tasks"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "the open vision computer an integrated sensing and compute system for mobile robots"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "aop an anti overfitting pretreatment for practical image based plant diagnosis"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "an in field automatic wheat disease diagnosis system"
    - "ssd single shot multibox detector"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "an open access repository of images on plant health to enable the development of mobile disease diagnostics"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

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)