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: "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
  Abstract: Deep learning approaches to 3D shape segmentation are typically formulated as a multi-class labeling problem. Existing models are trained for a fixed set of labels, which greatly limits their flexibility and adaptivity. We opt for top-down recursive decomposition and develop the first deep learning model for hierarchical segmentation of 3D shapes, based on recursive neural networks. Starting from a full shape represented as a point cloud, our model performs recursive binary decomposition, where the decomposition network at all nodes in the hierarchy share weights. At each node, a node classifier is trained to determine the type (adjacency or symmetry) and stopping criteria of its decomposition. The features extracted in higher level nodes are recursively propagated to lower level ones. Thus, the meaningful decompositions in higher levels provide strong contextual cues constraining the segmentations in lower levels. Meanwhile, to increase the segmentation accuracy at each node, we enhance the recursive contextual feature with the shape feature extracted for the corresponding part. Our method segments a 3D shape in point cloud into an unfixed number of parts, depending on the shape complexity, showing strong generality and flexibility. It achieves the state-of-the-art performance, both for fine-grained and semantic segmentation, on the public benchmark and a new benchmark of fine-grained segmentation proposed in this work. We also demonstrate its application for fine-grained part refinements in image-to-shape reconstruction.

Connected through hyperedge HE-1 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pq net a generative part seq2seq network for 3d shapes"
    - "object driven text to image synthesis via adversarial training"
    - "cvxnets learnable convex decomposition"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "learning to infer and execute 3d shape programs"

Connected through hyperedge HE-2 (induced by source paper "learning to segment 3d point clouds in 2d image space"):
  with members:
    - "jsis3d joint semantic instance segmentation of 3d point clouds with multi task pointwise networks and multi value conditional random fields"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "interpolated convolutional networks for 3d point cloud understanding"
    - "densepoint learning densely contextual representation for efficient point cloud processing"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "octree guided cnn with spherical kernels for 3d point clouds"
    - "kpconv flexible and deformable convolution for point clouds"
    - "hierarchical point edge interaction network for point cloud semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning to infer and execute 3d shape programs"
    - "meshcnn a network with an edge"
    - "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-4 (induced by source paper "cvxnets learnable convex decomposition"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "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"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "generating 3d faces using convolutional mesh autoencoders"

Connected through hyperedge HE-5 (induced by source paper "learning part generation and assembly for structure aware shape synthesis"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "structure aware shape synthesis"
    - "learning fuzzy set representations of partial shapes on dual embedding spaces"
    - "learning implicit fields for generative shape modeling"
    - "learning to generate the unseen via part synthesis and composition"
    - "learning free form deformations for 3d object reconstruction"

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"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "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 "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"):
  with members:
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "voxsegnet volumetric cnns for semantic part segmentation of 3d shapes"
    - "virtualhome simulating household activities via programs"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "chalet cornell house agent learning environment"
    - "splatnet sparse lattice networks for point cloud processing"
    - "dynamic graph cnn for learning on point clouds"
    - "structure aware generative network for 3d shape modeling"

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)