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: "scores shape composition with recursive substructure priors"
  Abstract: We introduce SCORES, a recursive neural network for shape composition. Our network takes as input sets of parts from two or more source 3D shapes and a rough initial placement of the parts. It outputs an optimized part structure for the composed shape, leading to high-quality geometry construction. A unique feature of our composition network is that it is not merely learning how to connect parts. Our goal is to produce a coherent and plausible 3D shape, despite large incompatibilities among the input parts. The network may significantly alter the geometry and structure of the input parts and synthesize a novel shape structure based on the inputs, while adding or removing parts to minimize a structure plausibility loss. We design SCORES as a recursive autoencoder network. During encoding, the input parts are recursively grouped to generate a root code. During synthesis, the root code is decoded, recursively, to produce a new, coherent part assembly. Assembled shape structures may be novel, with little global resemblance to training exemplars, yet have plausible substructures. SCORES therefore learns a hierarchical substructure shape prior based on per-node losses. It is trained on structured shapes from ShapeNet, and is applied iteratively to reduce the plausibility loss.We showresults of shape composition from multiple sources over different categories of man-made shapes and compare with state-of-the-art alternatives, demonstrating that our network can significantly expand the range of composable shapes for assembly-based modeling.

Connected through hyperedge HE-1 (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-2 (induced by source paper "partnet a recursive part decomposition network for fine grained and hierarchical shape segmentation"):
  with members:
    - "learning to group and label fine grained shape components"
    - "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"
    - "point convolutional neural networks by extension operators"
    - "grains generative recursive autoencoders for indoor scenes"
    - "splatnet sparse lattice networks for point cloud processing"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-3 (induced by source paper "learning to infer and execute 3d shape programs"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "physical primitive decomposition"
    - "learning to adapt meta learning for model based control"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "robustfill neural program learning under noisy i o"
    - "inferring and executing programs for visual reasoning"
    - "making neural programming architectures generalize via recursion"

Connected through hyperedge HE-4 (induced by source paper "read recursive autoencoders for document layout generation"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "grains generative recursive autoencoders for indoor scenes"
    - "a style based generator architecture for generative adversarial networks"
    - "mask r cnn"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "learning implicit fields for generative shape modeling"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multiresolution tree networks for 3d point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"):
  with members:
    - "dynamic graph convolutional networks"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "dynamic graph cnn for learning on point clouds"
    - "pu net point cloud upsampling network"
    - "tangent convolutions for dense prediction in 3d"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-7 (induced by source paper "learning to generate the unseen via part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "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"
    - "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-8 (induced by source paper "componet learning to generate the unseen by part synthesis and composition"):
  with members:
    - "composite shape modeling via latent space factorization"
    - "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"
    - "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.GR(Graphics)