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: "interactive 3d modeling with a generative adversarial network"
  Abstract: This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface. The user edits a voxel grid with a painting interface (like Minecraft). Yet, at any time, he/she can execute a SNAP command, which projects the current voxel grid onto a latent shape manifold with a learned projection operator and then generates a similar, but more realistic, shape using a learned generator network. Then the user can edit the resulting shape and snap again until he/she is satisfied with the result. The main advantage of this approach is that the projection and generation operators assist novice users to create 3D models characteristic of a background distribution of object shapes, but without having to specify all the details. The core new research idea is to use a GAN to support this application. 3D GANs have previously been used for shape generation, interpolation, and completion, but never for interactive modeling. The new challenge for this application is to learn a projection operator that takes an arbitrary 3D voxel model and produces a latent vector on the shape manifold from which a similar and realistic shape can be generated. We develop algorithms for this and other steps of the SNAP processing pipeline and integrate them into a simple modeling tool. Experiments with these algorithms and tool suggest that GANs provide a promising approach to computer-assisted interactive modeling.

Connected through hyperedge HE-1 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-2 (induced by source paper "coherent point drift networks unsupervised learning of non rigid point set registration"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "an unsupervised learning model for deformable medical image registration"
    - "began boundary equilibrium generative adversarial networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "convolutional neural network architecture for geometric matching"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"

Connected through hyperedge HE-3 (induced by source paper "multi chart generative surface modeling"):
  with members:
    - "deep sets"
    - "progressive growing of gans for improved quality stability and variation"
    - "deformable shape completion with graph convolutional autoencoders"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "learning representations and generative models for 3d point clouds"
    - "surfnet generating 3d shape surfaces using deep residual networks"
    - "geometric deep learning going beyond euclidean data"
    - "3d shape reconstruction from sketches via multi view convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning for determining a near optimal topological design without any iteration"):
  with members:
    - "neural networks for topology optimization"
    - "progressive growing of gans for improved quality stability and variation"
    - "deep learning the physics of transport phenomena"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "tacotron towards end to end speech synthesis"
    - "machine learning augmented predictive modeling of turbulent separated flows over airfoils"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accelerating eulerian fluid simulation with convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "minerl a large scale dataset of minecraft demonstrations"):
  with members:
    - "learning dexterous in hand manipulation"
    - "rainbow combining improvements in deep reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the atari grand challenge dataset"
    - "hierarchical and interpretable skill acquisition in multi task reinforcement learning"
    - "stardata a starcraft ai research dataset"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-6 (induced by source paper "text2shape generating shapes from natural language by learning joint embeddings"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "learning by association a versatile semi supervised training method for neural networks"
    - "learning robust visual semantic embeddings"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "deep metric learning with angular loss"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "learning free form deformations for 3d object reconstruction"):
  with members:
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "compact model representation for 3d reconstruction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-8 (induced by source paper "image2mesh a learning framework for single image 3d reconstruction"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "compact model representation for 3d reconstruction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "o cnn octree based convolutional neural networks for 3d shape analysis"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

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)