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: "superquadrics revisited learning 3d shape parsing beyond cuboids"
  Abstract: Abstracting complex 3D shapes with parsimonious part-based representations has been a long standing goal in computer vision. This paper presents a learning-based solution to this problem which goes beyond the traditional 3D cuboid representation by exploiting superquadrics as atomic elements. We demonstrate that superquadrics lead to more expressive 3D scene parses while being easier to learn than 3D cuboid representations. Moreover, we provide an analytical solution to the Chamfer loss which avoids the need for computational expensive reinforcement learning or iterative prediction. Our model learns to parse 3D objects into consistent superquadric representations without supervision. Results on various ShapeNet categories as well as the SURREAL human body dataset demonstrate the flexibility of our model in capturing fine details and complex poses that could not have been modelled using cuboids.

Connected through hyperedge HE-1 (induced by source paper "total3dunderstanding joint layout object pose and mesh reconstruction for indoor scenes from a single image"):
  with members:
    - "3d scene reconstruction with multi layer depth and epipolar transformers"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning elementary structures for 3d shape generation and matching"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "learning to infer and execute 3d shape programs"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "few shot generalization for single image 3d reconstruction via priors"
    - "deep level sets implicit surface representations for 3d shape inference"

Connected through hyperedge HE-2 (induced by source paper "segmentation and recovery of superquadric models using convolutional neural networks"):
  with members:
    - "recovery of superquadrics from range images using deep learning a preliminary study"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "mask r cnn"
    - "learning shape abstractions by assembling volumetric primitives"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "cvxnets learnable convex decomposition"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "interactive differentiable simulation"
    - "what do single view 3d reconstruction networks learn"
    - "attentive context normalization for robust permutation equivariant learning"
    - "bae net branched autoencoder for shape co segmentation"
    - "generating 3d faces using convolutional mesh autoencoders"

Connected through hyperedge HE-5 (induced by source paper "deep structured implicit functions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "mesh r cnn"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-6 (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"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "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-7 (induced by source paper "neural shape parsers for constructive solid geometry"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "learning to infer and execute 3d shape programs"
    - "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-8 (induced by source paper "bsp net generating compact meshes via binary space partitioning"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "logan unpaired shape transform in latent overcomplete space"
    - "bae net branched autoencoder for shape co segmentation"
    - "sdm net deep generative network for structured deformable mesh"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "im2struct recovering 3d shape structure from a single rgb image"

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)