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: "exploring invariances in deep convolutional neural networks using synthetic images"
  Abstract: Crowdsourced 3D CAD models are becoming easily accessible online, and can potentially generate an infinite number of training images for almost any object category. We show that adapting contemporary Deep Convolutional Neural Net (DCNN) models to such data can be effective, especially in the few-shot regime where none or only a few annotated real images are available, or where the images are not well matched to the target domain. Little is known about the degree of realism necessary to train models with deep features on CAD data. In a detailed analysis, we use synthetic images to probe DCNN invariance to object-class variations caused by 3D shape, pose, and photorealism, with surprising findings. In particular, we show that DCNNs used as a fixed representation exhibit a large amount of invariance to these factors, but, if allowed to adapt, can still learn effectively from synthetic data. These findings guide us in designing a method for adaptive training of DCNNs using real and synthetic data. We show that our approach significantly outperforms previous methods on the benchmark PASCAL VOC2007 dataset when learning in the fewshot scenario, and outperform training with real data in a domain shift scenario on the Office benchmark.

Connected through hyperedge HE-1 (induced by source paper "metamorphic testing for object detection systems"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "yolact real time instance segmentation"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "symbolic execution for deep neural networks"
    - "deep learning for generic object detection a survey"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-2 (induced by source paper "generative model with coordinate metric learning for object recognition based on 3d models"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "shapenet an information rich 3d model repository"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "context augmentation for convolutional neural networks"):
  with members:
    - "deep image scaling up image recognition"
    - "understanding deep image representations by inverting them"
    - "how transferable are features in deep neural networks"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "very deep convolutional networks for large scale image recognition"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "striving for simplicity the all convolutional net"
    - "understanding image representations by measuring their equivariance and equivalence"

Connected through hyperedge HE-4 (induced by source paper "deep domain adaptation based video smoke detection using synthetic smoke images"):
  with members:
    - "how useful is photo realistic rendering for visual learning"
    - "deep coral correlation alignment for deep domain adaptation"
    - "deepid3 face recognition with very deep neural networks"
    - "simultaneous deep transfer across domains and tasks"
    - "deep residual learning for image recognition"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "deep exemplar 2d 3d detection by adapting from real to rendered views"
    - "deep domain confusion maximizing for domain invariance"

Connected through hyperedge HE-5 (induced by source paper "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "unsupervised learning of depth and ego motion from video"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "learning 3d object categories by looking around them"
    - "orientation boosted voxel nets for 3d object recognition"
    - "generative and discriminative voxel modeling with convolutional neural networks"
    - "beam search for learning a deep convolutional neural network of 3d shapes"

Connected through hyperedge HE-6 (induced by source paper "single image 3d interpreter network"):
  with members:
    - "stacked hourglass networks for human pose estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "3dmatch learning local geometric descriptors from rgb d reconstructions"
    - "learning dense correspondence via 3d guided cycle consistency"
    - "spatial transformer networks"
    - "deep convolutional inverse graphics network"
    - "a dual source approach for 3d pose estimation from a single image"

Connected through hyperedge HE-7 (induced by source paper "learning a predictable and generative vector representation for objects"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "single image 3d interpreter network"
    - "fpnn field probing neural networks for 3d data"
    - "inferring 3d object pose in rgb d images"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep convolutional inverse graphics network"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"

Connected through hyperedge HE-8 (induced by source paper "deep networks can resemble human feed forward vision in invariant object recognition"):
  with members:
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "deep learning in neural networks"
    - "towards biologically plausible deep learning"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "discovering hidden factors of variation in deep networks"

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)