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: "meshcnn a network with an edge"
  Abstract: Polygonal meshes provide an efficient representation for 3D shapes. They explicitly captureboth shape surface and topology, and leverage non-uniformity to represent large flat regions as well as sharp, intricate features. This non-uniformity and irregularity, however, inhibits mesh analysis efforts using neural networks that combine convolution and pooling operations. In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes. Analogous to classic CNNs, MeshCNN combines specialized convolution and pooling layers that operate on the mesh edges, by leveraging their intrinsic geodesic connections. Convolutions are applied on edges and the four edges of their incident triangles, and pooling is applied via an edge collapse operation that retains surface topology, thereby, generating new mesh connectivity for the subsequent convolutions. MeshCNN learns which edges to collapse, thus forming a task-driven process where the network exposes and expands the important features while discarding the redundant ones. We demonstrate the effectiveness of MeshCNN on various learning tasks applied to 3D meshes.

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 "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deep hough voting for 3d object detection in point clouds"
    - "bodynet volumetric inference of 3d human body shapes"

Connected through hyperedge HE-3 (induced by source paper "continuous geodesic convolutions for learning on 3d shapes"):
  with members:
    - "3d local features for direct pairwise registration"
    - "kpconv flexible and deformable convolution for point clouds"
    - "quaternion equivariant capsule networks for 3d point clouds"
    - "self supervised learning of dense shape correspondence"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "point convolutional neural networks by extension operators"
    - "multi directional geodesic neural networks via equivariant convolution"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"

Connected through hyperedge HE-4 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "learning neural pde solvers with convergence guarantees"
    - "a deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials"
    - "one shot generation of near optimal topology through theory driven machine learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural networks for topology optimization"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

Connected through hyperedge HE-5 (induced by source paper "adversarial generation of continuous implicit shape representations"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep meta functionals for shape representation"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "a survey on deep learning advances on different 3d data representations"
    - "occupancy networks learning 3d reconstruction in function space"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-6 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "learning to infer and execute 3d shape programs"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "grains generative recursive autoencoders for indoor scenes"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"

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"
    - "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"
    - "adaptive o cnn a patch based deep representation of 3d shapes"

Connected through hyperedge HE-8 (induced by source paper "higher order function networks for learning composable 3d object representations"):
  with members:
    - "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"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "learning to reconstruct shapes from unseen classes"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "occupancy networks learning 3d reconstruction in function space"
    - "learning efficient point cloud generation for dense 3d object reconstruction"

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.LG(Machine Learning)