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: "3d aware scene manipulation via inverse graphics"
  Abstract: We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene representations learned by neural networks are often uninterpretable, limited to a single object, or lacking 3D knowledge. In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model. Our scene encoder performs inverse graphics, translating a scene into a structured object-wise representation. Our decoder has two components: a differentiable shape renderer and a neural texture generator. The disentanglement of semantics, geometry, and appearance supports 3D-aware scene manipulation, e.g., rotating and moving objects freely while keeping the consistent shape and texture, and changing the object appearance without affecting its shape. Experiments demonstrate that our editing scheme based on 3D-SDN is superior to its 2D counterpart.

Connected through hyperedge HE-1 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "a tree based decoder for neural machine translation"
    - "automatic program synthesis of long programs with a learned garbage collector"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

Connected through hyperedge HE-2 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep interpretable non rigid structure from motion"
    - "face alignment in full pose range a 3d total solution"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "inverserendernet learning single image inverse rendering"
    - "fml face model learning from videos"

Connected through hyperedge HE-3 (induced by source paper "ugan untraceable gan for multi domain face translation"):
  with members:
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "face aging with contextual generative adversarial nets"
    - "modular generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "a variational u net for conditional appearance and shape generation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-4 (induced by source paper "advspade realistic unrestricted attacks for semantic segmentation"):
  with members:
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "semantic image synthesis with spatially adaptive normalization"
    - "adaptive generation of unrestricted adversarial inputs"
    - "unified perceptual parsing for scene understanding"
    - "spectral normalization for generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "generating adversarial examples with adversarial networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "semantic hierarchy emerges in the deep generative representations for scene synthesis"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "singan learning a generative model from a single natural image"
    - "edge semantic learning strategy for layout estimation in indoor environment"
    - "ganalyze toward visual definitions of cognitive image properties"
    - "semantic image synthesis with spatially adaptive normalization"
    - "interpreting the latent space of gans for semantic face editing"
    - "unified perceptual parsing for scene understanding"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "differentiable surface splatting for point based geometry processing"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "inverse path tracing for joint material and lighting estimation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "unsupervised learning of shape and pose with differentiable point clouds"
    - "ec net an edge aware point set consolidation network"

Connected through hyperedge HE-7 (induced by source paper "generating semantic adversarial examples with differentiable rendering"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "should adversarial attacks use pixel p norm"
    - "certified adversarial robustness via randomized smoothing"
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "counterexample guided data augmentation"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-8 (induced by source paper "learning to predict 3d objects with an interpolation based differentiable renderer"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "what do single view 3d reconstruction networks learn"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning category specific mesh reconstruction from image collections"
    - "pixel2mesh generating 3d mesh models from single rgb 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)