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: "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
  Abstract: Performance on benchmark datasets has drastically improved with advances in deep learning. Still, cross-dataset generalization performance remains relatively low due to the domain shift that can occur between two different datasets. This domain shift is especially exaggerated between synthetic and real datasets. Significant research has been done to reduce this gap, specifically via modeling variation in the spatial layout of a scene, such as occlusions, and scene environmental factors, such as time of day and weather effects. However, few works have addressed modeling the variation in the sensor domain as a means of reducing the synthetic to real domain gap. The camera or sensor used to capture a dataset introduces artifacts into the image data that are unique to the sensor model, suggesting that sensor effects may also contribute to domain shift. To address this, we propose a learned augmentation network composed of physically-based augmentation functions. Our proposed augmentation pipeline transfers specific effects of the sensor model -- chromatic aberration, blur, exposure, noise, and color temperature -- from a real dataset to a synthetic dataset. We provide experiments that demonstrate that augmenting synthetic training datasets with the proposed learned augmentation framework reduces the domain gap between synthetic and real domains for object detection in urban driving scenes.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "continuous control with contexts provably"
    - "learning agile and dynamic motor skills for legged robots"
    - "h_ infty model free reinforcement learning with robust stability guarantee"
    - "robust model free reinforcement learning with multi objective bayesian optimization"
    - "sim to real learning agile locomotion for quadruped robots"

Connected through hyperedge HE-2 (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"
    - "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"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "neural network generalization the impact of camera parameters"):
  with members:
    - "a system for generating complex physically accurate sensor images for automotive applications"
    - "soft prototyping camera designs for car detection based on a convolutional neural network"
    - "aads augmented autonomous driving simulation using data driven algorithms"
    - "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"

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)