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: "a system for generating complex physically accurate sensor images for automotive applications"
  Abstract: We describe an open-source simulator that creates sensor irradiance and sensor images of typical automotive scenes in urban settings. The purpose of the system is to support camera design and testing for automotive applications. The user can specify scene parameters (e.g., scene type, road type, traffic density, time of day) to assemble a large number of random scenes from graphics assets stored in a database. The sensor irradiance is generated using quantitative computer graphics methods, and the sensor images are created using image systems sensor simulation. The synthetic sensor images have pixel level annotations; hence, they can be used to train and evaluate neural networks for imaging tasks, such as object detection and classification. The end-to-end simulation system supports quantitative assessment, from scene to camera to network accuracy, for automotive applications.

Connected through hyperedge HE-1 (induced by source paper "generating all the roads to rome road layout randomization for improved road marking segmentation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "aads augmented autonomous driving simulation using data driven algorithms"
    - "augmented lidar simulator for autonomous driving"
    - "streetgen in base city scale procedural generation of streets road network road surface and street objects"
    - "3d lanenet end to end 3d multiple lane detection"
    - "vadra visual adversarial domain randomization and augmentation"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "soft prototyping camera designs for car detection based on a convolutional neural network"):
  with members:
    - "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"
    - "procedural modeling and physically based rendering for synthetic data generation in automotive applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "neural network generalization the impact of camera parameters"):
  with members:
    - "soft prototyping camera designs for car detection based on a convolutional neural network"
    - "aads augmented autonomous driving simulation using data driven algorithms"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "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"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"

Connected through hyperedge HE-4 (induced by source paper "comparing pattern sensitivity of a convolutional neural network with an ideal observer and support vector machine"):
  with members:
    - "progressive growing of gans for improved quality stability and variation"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "network in network"

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)