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: "an open access repository of images on plant health to enable the development of mobile disease diagnostics"
  Abstract: Human society needs to increase food production by an estimated 70% by 2050 to feed an expected population size that is predicted to be over 9 billion people. Currently, infectious diseases reduce the potential yield by an average of 40% with many farmers in the developing world experiencing yield losses as high as 100%. The widespread distribution of smartphones among crop growers around the world with an expected 5 billion smartphones by 2020 offers the potential of turning the smartphone into a valuable tool for diverse communities growing food. One potential application is the development of mobile disease diagnostics through machine learning and crowdsourcing. Here we announce the release of over 50,000 expertly curated images on healthy and infected leaves of crops plants through the existing online platform PlantVillage. We describe both the data and the platform. These data are the beginning of an on-going, crowdsourcing effort to enable computer vision approaches to help solve the problem of yield losses in crop plants due to infectious diseases.

Connected through hyperedge HE-1 (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-2 (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"
    - "using deep learning for image based plant disease detection"
    - "ssd single shot multibox detector"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a comparable study intrinsic difficulties of practical plant diagnosis from wide angle images"):
  with members:
    - "an in field automatic wheat disease diagnosis system"
    - "using deep learning for image based plant disease detection"
    - "ssd single shot multibox detector"
    - "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-4 (induced by source paper "deep learning for classification and severity estimation of coffee leaf biotic stress"):
  with members:
    - "a smartphone application to detection and classification of coffee leaf miner and coffee leaf rust"
    - "data augmentation by pairing samples for images classification"
    - "mixup beyond empirical risk minimization"
    - "an overview of multi task learning in deep neural networks"
    - "using deep learning for image based plant disease detection"

Connected through hyperedge HE-5 (induced by source paper "using deep learning for image based plant disease detection"):
  with members:
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "visualizing and understanding convolutional networks"
    - "network in network"

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.CY(Computers and Society)