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: "supervised fitting of geometric primitives to 3d point clouds"
  Abstract: Fitting geometric primitives to 3D point cloud data bridges a gap between low-level digitized 3D data and high-level structural information on the underlying 3D shapes. As such, it enables many downstream applications in 3D data processing. For a long time, RANSAC-based methods have been the gold standard for such primitive fitting problems, but they require careful per-input parameter tuning and thus do not scale well for large datasets with diverse shapes. In this work, we introduce Supervised Primitive Fitting Network (SPFN), an end-to-end neural network that can robustly detect a varying number of primitives at different scales without any user control. The network is supervised using ground truth primitive surfaces and primitive membership for the input points. Instead of directly predicting the primitives, our architecture first predicts per-point properties and then uses a differential model estimation module to compute the primitive type and parameters. We evaluate our approach on a novel benchmark of ANSI 3D mechanical component models and demonstrate a significant improvement over both the state-of-the-art RANSAC-based methods and the direct neural prediction.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of intrinsic structural representation points"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "learning elementary structures for 3d shape generation and matching"
    - "unsupervised part based disentangling of object shape and appearance"
    - "bae net branched autoencoder for shape co segmentation"
    - "3d point cloud generative adversarial network based on tree structured graph convolutions"
    - "3d point capsule networks"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"

Connected through hyperedge HE-2 (induced by source paper "learning shape templates with structured implicit functions"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "learning category specific mesh reconstruction from image collections"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "parsing geometry using structure aware shape templates"
    - "occupancy networks learning 3d reconstruction in function space"
    - "3d prnn generating shape primitives with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep parametric shape predictions using distance fields"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "vectorization of line drawings via polyvector fields"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-4 (induced by source paper "neural shape parsers for constructive solid geometry"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning to infer and execute 3d shape programs"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning to infer graphics programs from hand drawn images"

Connected through hyperedge HE-5 (induced by source paper "learning structural graph layouts and 3d shapes for long span bridges 3d reconstruction"):
  with members:
    - "primitive based 3d building modeling sensor simulation and estimation"
    - "what do single view 3d reconstruction networks learn"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "multi view silhouette and depth decomposition for high resolution 3d object representation"
    - "pix3d dataset and methods for single image 3d shape modeling"

Connected through hyperedge HE-6 (induced by source paper "unsupervised cycle consistent deformation for shape matching"):
  with members:
    - "shape2motion joint analysis of motion parts and attributes from 3d shapes"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "open3d a modern library for 3d data processing"
    - "continuous and orientation preserving correspondences via functional maps"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-7 (induced by source paper "learning elementary structures for 3d shape generation and matching"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "learning category specific mesh reconstruction from image collections"
    - "3d coded 3d correspondences by deep deformation"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "foldingnet point cloud auto encoder via deep grid deformation"
    - "csgnet neural shape parser for constructive solid geometry"

Connected through hyperedge HE-8 (induced by source paper "automatic 3d mapping for tree diameter measurements in inventory operations"):
  with members:
    - "tree species identification from bark images using convolutional neural networks"

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)