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: "composite shape modeling via latent space factorization"
  Abstract: We present a novel neural network architecture, termed Decomposer-Composer, for semantic structure-aware 3D shape modeling. Our method utilizes an auto-encoder-based pipeline, and produces a novel factorized shape embedding space, where the semantic structure of the shape collection translates into a data-dependent sub-space factorization, and where shape composition and decomposition become simple linear operations on the embedding coordinates. We further propose to model shape assembly using an explicit learned part deformation module, which utilizes a 3D spatial transformer network to perform an in-network volumetric grid deformation, and which allows us to train the whole system end-to-end. The resulting network allows us to perform part-level shape manipulation, unattainable by existing approaches. Our extensive ablation study, comparison to baseline methods and qualitative analysis demonstrate the improved performance of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "structurenet hierarchical graph networks for 3d shape generation"):
  with members:
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "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-2 (induced by source paper "pq net a generative part seq2seq network for 3d shapes"):
  with members:
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning part generation and assembly for structure aware shape synthesis"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "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-4 (induced by source paper "latent feature disentanglement for 3d meshes"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "exploring disentangled feature representation beyond face identification"
    - "dgpose disentangled semi supervised deep generative models for human body analysis"
    - "everybody dance now"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "decomposing motion and content for natural video sequence prediction"
    - "building statistical shape spaces for 3d human modeling"

Connected through hyperedge HE-5 (induced by source paper "shapeglot learning language for shape differentiation"):
  with members:
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "learning representations and generative models for 3d point clouds"
    - "colors in context a pragmatic neural model for grounded language understanding"
    - "shapenet an information rich 3d model repository"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "learning to generate the unseen via part synthesis and composition"):
  with members:
    - "learning part generation and assembly for structure aware shape synthesis"
    - "image inpainting for irregular holes using partial convolutions"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "scores shape composition with recursive substructure priors"
    - "compositional gan learning image conditional binary composition"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "componet learning to generate the unseen by part synthesis and composition"):
  with members:
    - "learning part generation and assembly for structure aware shape synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "compositional gan learning conditional image composition"
    - "scores shape composition with recursive substructure priors"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "began boundary equilibrium generative adversarial networks"
    - "multi generator generative adversarial nets"

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)