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: "image augmentation using radial transform for training deep neural networks"
  Abstract: Deep learning models have a large number of free parameters that must be estimated by efficient training of the models on a large number of training data samples to increase their generalization performance. In real-world applications, the data available to train these networks is often limited or imbalanced. We propose a sampling method based on the radial transform in a polar coordinate system for image augmentation to facilitate the training of deep learning models from limited source data. This pixel-wise transform provides representations of the original image in the polar coordinate system by generating a new image from each pixel. This technique can generate radial transformed images up to the number of pixels in the original image to increase the diversity of poorly represented image classes. Our experiments show improved generalization performance in training deep convolutional neural networks with radial transformed images.

Connected through hyperedge HE-1 (induced by source paper "deep complex networks for protocol agnostic radio frequency device fingerprinting in the wild"):
  with members:
    - "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms"
    - "gated complex recurrent neural networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "complex gated recurrent neural networks"
    - "an introduction to deep learning for the physical layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "deep complex networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-2 (induced by source paper "a deep gradient boosting network for optic disc and cup segmentation"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "deformable convolutional networks"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "ising dropout a regularization method for training and compression of deep neural networks"):
  with members:
    - "recent advances in recurrent neural networks"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"

Connected through hyperedge HE-4 (induced by source paper "cylindrical transform 3d semantic segmentation of kidneys with limited annotated images"):
  with members:
    - "recent advances in recurrent neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"
    - "interpretation of mammogram and chest radiograph reports using deep neural networks preliminary results"
    - "deep recurrent neural networks for sequential phenotype prediction in genomics"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "disc aware ensemble network for glaucoma screening from fundus image"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "polar transformer networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"):
  with members:
    - "learning representations of emotional speech with deep convolutional generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep recurrent neural networks for sequential phenotype prediction in genomics"

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)