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 image scaling up image recognition"
  Abstract: We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images. Our method achieves excellent results on multiple challenging computer vision benchmarks.

Connected through hyperedge HE-1 (induced by source paper "stochastic optimization of plain convolutional neural networks with simple methods"):
  with members:
    - "smart augmentation learning an optimal data augmentation strategy"
    - "an analysis of deep neural network models for practical applications"
    - "improved regularization of convolutional neural networks with cutout"
    - "apac augmented pattern classification with neural networks"
    - "efficient object localization using convolutional networks"
    - "deeppose human pose estimation via deep neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-2 (induced by source paper "online hyper parameter learning for auto augmentation strategy"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "autoaugment learning augmentation policies from data"
    - "darts differentiable architecture search"
    - "irlas inverse reinforcement learning for architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "mnasnet platform aware neural architecture search for mobile"
    - "data augmentation in emotion classification using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "tictac accelerating distributed deep learning with communication scheduling"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-4 (induced by source paper "the robotrix an extremely photorealistic and very large scale indoor dataset of sequences with robot trajectories and interactions"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"

Connected through hyperedge HE-5 (induced by source paper "object recognition under multifarious conditions a reliability analysis and a feature similarity based performance estimation"):
  with members:
    - "challenging environments for traffic sign detection reliability assessment under inclement conditions"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "traffic signs in the wild highlights from the ieee video and image processing cup 2017 student competition sp competitions"
    - "cure or challenging unreal and real environments for object recognition"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cure tsr challenging unreal and real environments for traffic sign recognition"
    - "on classification of distorted images with deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "cross domain image classification through neural style transfer data augmentation"):
  with members:
    - "a survey on face data augmentation"
    - "data augmentation using gans"
    - "stada style transfer as data augmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "effects of degradations on deep neural network architectures"
    - "style augmentation data augmentation via style randomization"
    - "domain adaptation for visual applications a comprehensive survey"
    - "demystifying neural style transfer"

Connected through hyperedge HE-7 (induced by source paper "learning from learning machines optimisation rules and social norms"):
  with members:
    - "deep learning vs traditional computer vision"
    - "tensor2tensor for neural machine translation"
    - "incomplete contracting and ai alignment"
    - "recent trends in deep learning based natural language processing"
    - "a survey on deep learning in medical image analysis"
    - "deep learning in bioinformatics"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep multimodal learning for audio visual speech recognition"

Connected through hyperedge HE-8 (induced by source paper "data augmentation with symbolic to real image translation gans for traffic sign recognition"):
  with members:
    - "applying data augmentation to handwritten arabic numeral recognition using deep learning neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "image to image translation with conditional adversarial networks"
    - "conditional generative adversarial nets"

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)