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: "structure aware generative network for 3d shape modeling"
  Abstract: We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships between them (the structure) are jointly learned and embedded in a latent space by an autoencoder. The encoder intertwines the geometry and structure features into a single latent code, while the decoder disentangles the features and reconstructs the geometry and structure of the 3D model. Our autoencoder consists of two branches, one for the structure and one for the geometry. The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code. This explicit intertwining of information enables separately controlling the geometry and the structure of the generated models. We evaluate the performance of our method and conduct an ablation study. We explicitly show that encoding of shapes accounts for both similarities in structure and geometry. A variety of quality results generated by SAGNet are presented.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "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"

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"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "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"
    - "learning representations and generative models for 3d point clouds"

Connected through hyperedge HE-4 (induced by source paper "learning embedding of 3d models with quadric loss"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "scan2mesh from unstructured range scans to 3d meshes"
    - "dynamic graph cnn for learning on point clouds"
    - "abc a big cad model dataset for geometric deep learning"
    - "deformable shape completion with graph convolutional autoencoders"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning representations and generative models for 3d point clouds"

Connected through hyperedge HE-5 (induced by source paper "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"):
  with members:
    - "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"
    - "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"

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.GR(Graphics)