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: "learning to predict 3d objects with an interpolation based differentiable renderer"
  Abstract: Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present {\emph DIB-R}, a differentiable rendering framework which allows gradients to be analytically computed for all pixels in an image. Key to our approach is to view foreground rasterization as a weighted interpolation of local properties and background rasterization as a distance-based aggregation of global geometry. Our approach allows for accurate optimization over vertex positions, colors, normals, light directions and texture coordinates through a variety of lighting models. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: this https URL

Connected through hyperedge HE-1 (induced by source paper "blockgan learning 3d object aware scene representations from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "genesis generative scene inference and sampling with object centric latent representations"
    - "a layer based sequential framework for scene generation with gans"
    - "transformable bottleneck networks"
    - "generating multiple objects at spatially distinct locations"
    - "neural voxel renderer learning an accurate and controllable rendering tool"
    - "emergence of object segmentation in perturbed generative models"
    - "escaping plato s cave 3d shape from adversarial rendering"

Connected through hyperedge HE-2 (induced by source paper "seqxy2seqz structure learning for 3d shapes by sequentially predicting 1d occupancy segments from 2d coordinates"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "texture fields learning texture representations in function space"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "what do single view 3d reconstruction networks learn"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"

Connected through hyperedge HE-3 (induced by source paper "end to end learning local multi view descriptors for 3d point clouds"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "3d local features for direct pairwise registration"
    - "view n gram network for 3d object retrieval"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "pytorch an imperative style high performance deep learning library"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-4 (induced by source paper "differentiable volumetric rendering learning implicit 3d representations without 3d supervision"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "texture fields learning texture representations in function space"
    - "kpconv flexible and deformable convolution for point clouds"

Connected through hyperedge HE-5 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural rendering and reenactment of human actor videos"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "texture fields learning texture representations in function space"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of probably symmetric deformable 3d objects from images in the wild"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "unsupervised generative 3d shape learning from natural images"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"

Connected through hyperedge HE-7 (induced by source paper "dist rendering deep implicit signed distance function with differentiable sphere tracing"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "texture fields learning texture representations in function space"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "dpsnet end to end deep plane sweep stereo"
    - "neural volumes learning dynamic renderable volumes from images"

Connected through hyperedge HE-8 (induced by source paper "sdfdiff differentiable rendering of signed distance fields for 3d shape optimization"):
  with members:
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "visual object networks image generation with disentangled 3d representation"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "learning implicit fields for generative shape modeling"

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)