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: "approximate bayesian image interpretation using generative probabilistic graphics programs"
  Abstract: The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex bottom-up processing pipelines. Here we show that it is possible to write short, simple probabilistic graphics programs that define flexible generative models and to automatically invert them to interpret real-world images. Generative probabilistic graphics programs consist of a stochastic scene generator, a renderer based on graphics software, a stochastic likelihood model linking the renderer's output and the data, and latent variables that adjust the fidelity of the renderer and the tolerance of the likelihood model. Representations and algorithms from computer graphics, originally designed to produce high-quality images, are instead used as the deterministic backbone for highly approximate and stochastic generative models. This formulation combines probabilistic programming, computer graphics, and approximate Bayesian computation, and depends only on general-purpose, automatic inference techniques. We describe two applications: reading sequences of degraded and adversarially obscured alphanumeric characters, and inferring 3D road models from vehicle-mounted camera images. Each of the probabilistic graphics programs we present relies on under 20 lines of probabilistic code, and supports accurate, approximately Bayesian inferences about ambiguous real-world images.

Connected through hyperedge HE-1 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "a deep representation for depth images from synthetic data"
    - "learning to recognize objects by retaining other factors of variation"

Connected through hyperedge HE-2 (induced by source paper "meta sim learning to generate synthetic datasets"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "virtualhome simulating household activities via programs"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "learning to simulate"
    - "multimodal unsupervised image to image translation"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (induced by source paper "a learned representation for scalable vector graphics"):
  with members:
    - "figr few shot image generation with reptile"
    - "tensor2tensor for neural machine translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "attention is all you need"
    - "film visual reasoning with a general conditioning layer"
    - "learning to infer graphics programs from hand drawn images"

Connected through hyperedge HE-4 (induced by source paper "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "siclope silhouette based clothed people"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-5 (induced by source paper "soft rasterizer a differentiable renderer for image based 3d reasoning"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning category specific mesh reconstruction from image collections"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-6 (induced by source paper "analytical derivatives for differentiable renderer 3d pose estimation by silhouette consistency"):
  with members:
    - "a novel self intersection penalty term for statistical body shape models and its applications in 3d pose estimation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

Connected through hyperedge HE-7 (induced by source paper "learning programmatically structured representations with perceptor gradients"):
  with members:
    - "towards symbolic reinforcement learning with common sense"
    - "isolating sources of disentanglement in variational autoencoders"
    - "programmatically interpretable reinforcement learning"
    - "deep learning for physical processes incorporating prior scientific knowledge"
    - "deep reinforcement learning a brief survey"
    - "learning to infer graphics programs from hand drawn images"
    - "using program induction to interpret transition system dynamics"
    - "beating atari with natural language guided reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "transflow learning repurposing flow models without retraining"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "few shot unsupervised image to image translation"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "density estimation using real nvp"
    - "conditional image generation with pixelcnn decoders"
    - "adversarial feature learning"

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.AI(Artificial Intelligence)