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: "on rendering synthetic images for training an object detector"
  Abstract: We propose a novel approach to generating synthetic training data.We introduce detector-specific similarity measures between real and synthetic images.We show that the choice of the similarity measure relies on the features used by the detector.We have tested our approach on different object detection tasks. We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available.

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 "balancing domain gap for object instance detection"):
  with members:
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multimodal unsupervised image to image translation"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "autodial automatic domain alignment layers"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "beyond sharing weights for deep domain adaptation"):
  with members:
    - "progressive neural networks"
    - "adversarial discriminative domain adaptation"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "cross stitch networks for multi task learning"
    - "correlation alignment for unsupervised domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "domain separation networks"

Connected through hyperedge HE-4 (induced by source paper "synthetic data generation and adaption for object detection in smart vending machines"):
  with members:
    - "specular to diffuse translation for multi view reconstruction"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "object detection using deep cnns trained on synthetic images"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "sim to real learning for casualty detection from ground projected point cloud data"):
  with members:
    - "casualty detection from 3d point cloud data for autonomous ground mobile rescue robots"
    - "resqbot a mobile rescue robot with immersive teleperception for casualty extraction"
    - "focal loss for dense object detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "megdet a large mini batch object detector"
    - "learning from synthetic humans"
    - "mask r cnn"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "unsupervised synthesis of anomalies in videos transforming the normal"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning features by watching objects move"
    - "learning temporal regularity in video sequences"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-7 (induced by source paper "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"):
  with members:
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "3d object instance recognition and pose estimation using triplet loss with dynamic margin"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "triple generative adversarial nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "hwnet v2 an efficient word image representation for handwritten documents"):
  with members:
    - "neural word search in historical manuscript collections"
    - "attribute cnns for word spotting in handwritten documents"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "matching handwritten document images"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)