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: "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
  Abstract: We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "augmented lidar simulator for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "efficient road lane marking detection with deep learning"
    - "few shot object detection via feature reweighting"

Connected through hyperedge HE-2 (induced by source paper "soft prototyping camera designs for car detection based on a convolutional neural network"):
  with members:
    - "a system for generating complex physically accurate sensor images for automotive applications"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "small target detection for search and rescue operations using distributed deep learning and synthetic data generation"):
  with members:
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "occluded object reconstruction for first responders with augmented reality glasses using conditional generative adversarial networks"
    - "automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model"
    - "feature fused ssd fast detection for small objects"
    - "deep neural networks for pattern recognition"
    - "detecting small signs from large images"
    - "unrealcv connecting computer vision to unreal engine"
    - "mocap guided data augmentation for 3d pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "deep object pose estimation for semantic robotic grasping of household objects"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "falling things a synthetic dataset for 3d object detection and pose estimation"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"

Connected through hyperedge HE-5 (induced by source paper "driving policy transfer via modularity and abstraction"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "adversarial discriminative sim to real transfer of visuo motor policies"
    - "virtual to real reinforcement learning for autonomous driving"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "gradient free policy architecture search and adaptation"
    - "mutual alignment transfer learning"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-6 (induced by source paper "geometric image synthesis"):
  with members:
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "stabilizing training of generative adversarial networks through regularization"
    - "learning from synthetic humans"
    - "photographic image synthesis with cascaded refinement networks"
    - "playing for benchmarks"

Connected through hyperedge HE-7 (induced by source paper "augmented lidar simulator for autonomous driving"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "meta sim learning to generate synthetic datasets"
    - "iou loss for 2d 3d object detection"
    - "mapless online detection of dynamic objects in 3d lidar"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"

Connected through hyperedge HE-8 (induced by source paper "auxiliary tasks in multi task learning"):
  with members:
    - "evaluation of cnn based single image depth estimation methods"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "carla an open urban driving simulator"
    - "an overview of multi task learning in deep neural networks"
    - "playing for benchmarks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"

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)