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: "learning part generation and assembly for structure aware shape synthesis"
  Abstract: Learning deep generative models for 3D shape synthesis is largely limited by the difficulty of generating plausible shapes with correct topology and reasonable geometry. Indeed, learning the distribution of plausible 3D shapes seems a daunting task for most existing holistic shape representation, given the significant topological variations of 3D objects even within the same shape category. Enlightened by the common view that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network which we call PAGENet. The network is composed of an array of per-part VAE-GANs, generating semantic parts composing a complete shape, followed by a part assembly module that estimates a transformation for each part to correlate and assemble them into a plausible structure. Through splitting the generation of part composition and part relations into separate networks, the difficulty of modeling structural variations of 3D shapes is greatly reduced. We demonstrate through extensive experiments that PAGENet generates 3D shapes with plausible, diverse and detailed structure, and show two prototype applications: semantic shape segmentation and shape set evolution.

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"
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "pq net a generative part seq2seq network for 3d shapes"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "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 "composite shape modeling via latent space factorization"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "gibson env real world perception for embodied agents"
    - "semantic structure and interpretability of word embeddings"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "ganimation anatomically aware facial animation from a single image"
    - "structure aware generative network for 3d shape modeling"
    - "learning representations and generative models for 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "learning to generate the unseen via part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "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-5 (induced by source paper "componet learning to generate the unseen by part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "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)