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: "sim4cv a photo realistic simulator for computer vision applications"
  Abstract: We present a photo-realistic training and evaluation simulator (Sim4CV) (http://www.sim4cv.org) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool.

Connected through hyperedge HE-1 (induced by source paper "learning to train with synthetic humans"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "learning human optical flow"
    - "improving spatiotemporal self supervision by deep reinforcement learning"
    - "image based synthesis for deep 3d human pose estimation"
    - "learning to refine human pose estimation"
    - "a closed form solution to photorealistic image stylization"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "how robust is 3d human pose estimation to occlusion"

Connected through hyperedge HE-2 (induced by source paper "procedural synthesis of remote sensing images for robust change detection with neural networks"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "targeted change detection in remote sensing images"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "focal loss for dense object detection"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "towards generalizing sensorimotor control across weather conditions"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "multimodal unsupervised image to image translation"
    - "on evaluation of embodied navigation agents"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "on offline evaluation of vision based driving models"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "modular vehicle control for transferring semantic information to unseen weather conditions using gans"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning on a budget 3d control and reasoning without a supercomputer"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "a study on overfitting in deep reinforcement learning"
    - "neural modular control for embodied question answering"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "deep reinforcement learning that matters"
    - "home a household multimodal environment"
    - "ray a distributed framework for emerging ai applications"

Connected through hyperedge HE-5 (induced by source paper "the robotrix an extremely photorealistic and very large scale indoor dataset of sequences with robot trajectories and interactions"):
  with members:
    - "unrealrox an extremely photorealistic virtual reality environment for robotics simulations and synthetic data generation"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "minos multimodal indoor simulator for navigation in complex environments"
    - "home a household multimodal environment"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "ai2 thor an interactive 3d environment for visual ai"

Connected through hyperedge HE-6 (induced by source paper "aads augmented autonomous driving simulation using data driven algorithms"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "augmented lidar simulator for autonomous driving"
    - "heter sim heterogeneous multi agent systems simulation by interactive data driven optimization"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-7 (induced by source paper "learning a controller fusion network by online trajectory filtering for vision based uav racing"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"
    - "teaching uavs to race end to end regression of agile controls in simulation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "agile autonomous driving using end to end deep imitation learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cad2rl real single image flight without a single real image"

Connected through hyperedge HE-8 (induced by source paper "pose estimation for texture less shiny objects in a single rgb image using synthetic training data"):
  with members:
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "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"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "real time seamless single shot 6d object pose prediction"
    - "labelfusion a pipeline for generating ground truth labels for real rgbd data of cluttered 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)