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: "atlasnet a papier m ach e approach to learning 3d surface generation"
  Abstract: We introduce a method for learning to generate the surface of 3D shapes. Our approach represents a 3D shape as a collection of parametric surface elements and, in contrast to methods generating voxel grids or point clouds, naturally infers a surface representation of the shape. Beyond its novelty, our new shape generation framework, AtlasNet, comes with significant advantages, such as improved precision and generalization capabilities, and the possibility to generate a shape of arbitrary resolution without memory issues. We demonstrate these benefits and compare to strong baselines on the ShapeNet benchmark for two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction from a still image. We also provide results showing its potential for other applications, such as morphing, parametrization, super-resolution, matching, and co-segmentation.

Connected through hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "disentangling monocular 3d object detection"
    - "multi path learning for object pose estimation across domains"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mesh r cnn"
    - "normalized object coordinate space for category level 6d object pose and size estimation"

Connected through hyperedge HE-2 (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"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "bae net branched autoencoder for shape co segmentation"
    - "3d point cloud generative adversarial network based on tree structured graph convolutions"
    - "3d point capsule networks"

Connected through hyperedge HE-3 (induced by source paper "learning joint reconstruction of hands and manipulated objects"):
  with members:
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-4 (induced by source paper "nasa neural articulated shape approximation"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "amass archive of motion capture as surface shapes"
    - "deferred neural rendering image synthesis using neural textures"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "unsupervised part based disentangling of object shape and appearance"
    - "what do single view 3d reconstruction networks learn"
    - "cerberus a multi headed derenderer"

Connected through hyperedge HE-5 (induced by source paper "multiview aggregation for learning category specific shape reconstruction"):
  with members:
    - "equivariant multi view networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-6 (induced by source paper "pose from shape deep pose estimation for arbitrary 3d objects"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "matching rgb images to cad models for object pose estimation"
    - "dynamic graph cnn for learning on point clouds"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-7 (induced by source paper "sdm net deep generative network for structured deformable mesh"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "vv net voxel vae net with group convolutions for point cloud segmentation"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multi directional geodesic neural networks via equivariant convolution"

Connected through hyperedge HE-8 (induced by source paper "disn deep implicit surface network for high quality single view 3d reconstruction"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "what do single view 3d reconstruction networks learn"
    - "a skeleton bridged deep learning approach for generating meshes of complex topologies from single rgb images"
    - "3dn 3d deformation network"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"

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)