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: "rendergan generating realistic labeled data"
  Abstract: Deep Convolutional Neuronal Networks (DCNNs) are showing remarkable performance on many computer vision tasks. Due to their large parameter space, they require many labeled samples when trained in a supervised setting. The costs of annotating data manually can render the use of DCNNs infeasible. We present a novel framework called RenderGAN that can generate large amounts of realistic, labeled images by combining a 3D model and the Generative Adversarial Network framework. In our approach, image augmentations (e.g. lighting, background, and detail) are learned from unlabeled data such that the generated images are strikingly realistic while preserving the labels known from the 3D model. We apply the RenderGAN framework to generate images of barcode-like markers that are attached to honeybees. Training a DCNN on data generated by the RenderGAN yields considerably better performance than training it on various baselines.

Connected through hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "feature critic networks for heterogeneous domain generalization"
    - "dense classification and implanting for few shot learning"
    - "meta learning with differentiable convex optimization"
    - "progressive domain adaptation for object detection"
    - "a closer look at few shot classification"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image to image translation with bi directional feature transformation"

Connected through hyperedge HE-2 (induced by source paper "deceptionnet network driven domain randomization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "dpod 6d pose object detector and refiner"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep model based 6d pose refinement in rgb"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning dexterous in hand manipulation"
    - "adversarial patch"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"

Connected through hyperedge HE-3 (induced by source paper "learning data augmentation strategies for object detection"):
  with members:
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "fast autoaugment"
    - "a fourier perspective on model robustness in computer vision"
    - "metaanchor learning to detect objects with customized anchors"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "randaugment practical automated data augmentation with a reduced search space"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"
    - "fast autoaugment"
    - "do imagenet classifiers generalize to imagenet"

Connected through hyperedge HE-5 (induced by source paper "randaugment practical data augmentation with no separate search"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "do imagenet classifiers generalize to imagenet"
    - "a fourier perspective on model robustness in computer vision"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "suppressing model overfitting for image super resolution networks"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "blind super resolution with iterative kernel correction"
    - "zoom to learn learn to zoom"
    - "camera lens super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "investigating backtranslation in neural machine translation"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "data augmentation by pairing samples for images classification"

Connected through hyperedge HE-7 (induced by source paper "scene text synthesis for efficient and effective deep network training"):
  with members:
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "spatial fusion gan for image synthesis"
    - "esir end to end scene text recognition via iterative image rectification"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "cycada cycle consistent adversarial domain adaptation"
    - "single shot text detector with regional attention"

Connected through hyperedge HE-8 (induced by source paper "conditional generative adversarial networks for data augmentation and adaptation in remotely sensed imagery"):
  with members:
    - "multimodal 3d object detection from simulated pretraining"
    - "domain adaptive generation of aircraft on satellite imagery via simulated and unsupervised learning"
    - "xview objects in context in overhead imagery"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "pioneer networks progressively growing generative autoencoder"
    - "deep learning in medical image analysis and multimodal learning for clinical decision support 4th international workshop dlmia 2018 and 8th international workshop ml cds 2018 held in conjunction with miccai 2018 granada spain september 20 2018 proceedings"
    - "a style based generator architecture for generative adversarial networks"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"

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.NE(Neural and Evolutionary Computing)