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: "object detection using deep cnns trained on synthetic images"
  Abstract: The need for large annotated image datasets for training Convolutional Neural Networks (CNNs) has been a significant impediment for their adoption in computer vision applications. We show that with transfer learning an effective object detector can be trained almost entirely on synthetically rendered datasets. We apply this strategy for detecting pack- aged food products clustered in refrigerator scenes. Our CNN trained only with 4000 synthetic images achieves mean average precision (mAP) of 24 on a test set with 55 distinct products as objects of interest and 17 distractor objects. A further increase of 12% in the mAP is obtained by adding only 400 real images to these 4000 synthetic images in the training set. A high degree of photorealism in the synthetic images was not essential in achieving this performance. We analyze factors like training data set size and 3D model dictionary size for their influence on detection performance. Additionally, training strategies like fine-tuning with selected layers and early stopping which affect transfer learning from synthetic scenes to real scenes are explored. Training CNNs with synthetic datasets is a novel application of high-performance computing and a promising approach for object detection applications in domains where there is a dearth of large annotated image data.

Connected through hyperedge HE-1 (induced by source paper "synthetic examples improve generalization for rare classes"):
  with members:
    - "low shot learning from imaginary 3d model"
    - "class balanced loss based on effective number of samples"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "learning category specific mesh reconstruction from image collections"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "recognition in terra incognita"

Connected through hyperedge HE-2 (induced by source paper "an annotation saved is an annotation earned using fully synthetic training for object instance detection"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "applying domain randomization to synthetic data for object category detection"
    - "transfer learning from synthetic to real images using variational autoencoders for precise position detection"
    - "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"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "cut paste and learn surprisingly easy synthesis for instance detection"

Connected through hyperedge HE-3 (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"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-4 (induced by source paper "leagueai improving object detector performance and flexibility through automatically generated training data and domain randomization"):
  with members:
    - "yolov3 an incremental improvement"
    - "structured domain randomization bridging the reality gap by context aware synthetic data"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "you only look once unified real time object detection"
    - "going deeper with convolutions"

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)