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 infer implicit surfaces without 3d supervision"
  Abstract: Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation, directly from 2D images. This is particularly interesting since the availability of 3D models is still limited compared to the massive amount of accessible 2D images, which is invaluable for training. The representation of 3D surfaces itself is a key factor for the quality and resolution of the 3D output. While explicit representations, such as point clouds and voxels, can span a wide range of shape variations, their resolutions are often limited. Mesh-based representations are more efficient but are limited by their ability to handle varying topologies. Implicit surfaces, however, can robustly handle complex shapes, topologies, and also provide flexible resolution control. We address the fundamental problem of learning implicit surfaces for shape inference without the need of 3D supervision. Despite their advantages, it remains nontrivial to (1) formulate a differentiable connection between implicit surfaces and their 2D renderings, which is needed for image-based supervision; and (2) ensure precise geometric properties and control, such as local smoothness. In particular, sampling implicit surfaces densely is also known to be a computationally demanding and very slow operation. To this end, we propose a novel ray-based field probing technique for efficient image-to-field supervision, as well as a general geometric regularizer for implicit surfaces, which provides natural shape priors in unconstrained regions. We demonstrate the effectiveness of our framework on the task of single-view image-based 3D shape digitization and show how we outperform state-of-the-art techniques both quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep geometry learning from a representation perspective"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "deep learning for 3d point clouds a survey"
    - "bsp net generating compact meshes via binary space partitioning"
    - "pixel2mesh multi view 3d mesh generation via deformation"

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"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "inverse graphics gan learning to generate 3d shapes from unstructured 2d data"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "escaping plato s cave 3d shape from adversarial rendering"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "towards unsupervised learning of generative models for 3d controllable image synthesis"

Connected through hyperedge HE-4 (induced by source paper "convolutional occupancy networks"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "learning shape templates with structured implicit functions"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "texture fields learning texture representations in function space"
    - "efficient learning on point clouds with basis point sets"
    - "pixel2mesh multi view 3d mesh generation via deformation"

Connected through hyperedge HE-5 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency"):
  with members:
    - "photo geometric autoencoding to learn 3d objects from unlabelled images"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "scops self supervised co part segmentation"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning view priors for single view 3d reconstruction"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-6 (induced by source paper "curriculum deepsdf"):
  with members:
    - "implicit geometric regularization for learning shapes"
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "on the power of curriculum learning in training deep networks"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "deep structured implicit functions"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-7 (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"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"

Connected through hyperedge HE-8 (induced by source paper "self supervised learning of 3d objects from natural images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "learning view priors for single view 3d reconstruction"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "learning category specific mesh reconstruction from image collections"
    - "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)