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: "unrealcv connecting computer vision to unreal engine"
  Abstract: Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (this http URL) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.

Connected through hyperedge HE-1 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dota a large scale dataset for object detection in aerial images"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"

Connected through hyperedge HE-2 (induced by source paper "cinemairsim a camera realistic robotics simulator for cinematographic purposes"):
  with members:
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "automated cinematography with unmanned aerial vehicles"
    - "airways optimization based planning of quadrotor trajectories according to high level user goals"
    - "generic drone control platform for autonomous capture of cinema scenes"
    - "webotstm professional mobile robot simulation"

Connected through hyperedge HE-3 (induced by source paper "show attend and translate unsupervised image translation with self regularization and attention"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "depth completion via deep basis fitting"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "depth coefficients for depth completion"
    - "dense depth posterior ddp from single image and sparse range"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "voiced depth completion from inertial odometry and vision"
    - "noise aware unsupervised deep lidar stereo fusion"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-5 (induced by source paper "few shot adaptive faster r cnn"):
  with members:
    - "few example object detection with model communication"
    - "semantic foggy scene understanding with synthetic data"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "lstd a low shot transfer detector for object detection"
    - "focal loss for dense object detection"
    - "few shot adversarial domain adaptation"
    - "when unsupervised domain adaptation meets tensor representations"

Connected through hyperedge HE-6 (induced by source paper "synthtext3d synthesizing scene text images from 3d virtual worlds"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-7 (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"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "focal loss for dense object detection"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-8 (induced by source paper "5d light field synthesis from a monocular video"):
  with members:
    - "synthesizing a 4d spatio angular consistent light field from a single image"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"

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)