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: "geometrics exploiting geometric structure for graph encoded objects"
  Abstract: Mesh models are a promising approach for encoding the structure of 3D objects. Current mesh reconstruction systems predict uniformly distributed vertex locations of a predetermined graph through a series of graph convolutions, leading to compromises with respect to performance or resolution. In this paper, we argue that the graph representation of geometric objects allows for additional structure, which should be leveraged for enhanced reconstruction. Thus, we propose a system which properly benefits from the advantages of the geometric structure of graph encoded objects by introducing (1) a graph convolutional update preserving vertex information; (2) an adaptive splitting heuristic allowing detail to emerge; and (3) a training objective operating both on the local surfaces defined by vertices as well as the global structure defined by the mesh. Our proposed method is evaluated on the task of 3D object reconstruction from images with the ShapeNet dataset, where we demonstrate state of the art performance, both visually and numerically, while having far smaller space requirements by generating adaptive meshes

Connected through hyperedge HE-1 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "3dn 3d deformation network"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "topology adaptive graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "dressing 3d humans using a conditional mesh vae gan"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "meshgan non linear 3d morphable models of faces"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "moulding humans non parametric 3d human shape estimation from single images"

Connected through hyperedge HE-3 (induced by source paper "graph element networks adaptive structured computation and memory"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "attentive neural processes"
    - "a comprehensive survey on graph neural networks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "graph neural networks a review of methods and applications"
    - "modular meta learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "flexible neural representation for physics prediction"

Connected through hyperedge HE-4 (induced by source paper "deep structured implicit functions"):
  with members:
    - "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"
    - "what do single view 3d reconstruction networks learn"
    - "mesh r cnn"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-5 (induced by source paper "learning to predict 3d objects with an interpolation based differentiable renderer"):
  with members:
    - "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"
    - "3d aware scene manipulation via inverse graphics"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-6 (induced by source paper "kaolin a pytorch library for accelerating 3d deep learning research"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "meshcnn a network with an edge"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "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 "surface hof surface reconstruction from a single image using higher order function networks"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "mesh r cnn"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-8 (induced by source paper "deeporgannet on the fly reconstruction and visualization of 3d 4d lung models from single view projections by deep deformation network"):
  with members:
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "single image tomography 3d volumes from 2d cranial x rays"
    - "image2mesh a learning framework for single image 3d reconstruction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformnet free form deformation network for 3d shape reconstruction from a single image"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "a point set generation network for 3d object reconstruction from a single 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)