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: "3d sketching using multi view deep volumetric prediction"
  Abstract: Sketch-based modeling strives to bring the ease and immediacy of drawing to the 3D world. However, while drawings are easy for humans to create, they are very challenging for computers to interpret due to their sparsity and ambiguity. We propose a data-driven approach that tackles this challenge by learning to reconstruct 3D shapes from one or more drawings. At the core of our approach is a deep convolutional neural network (CNN) that predicts occupancy of a voxel grid from a line drawing. This CNN provides us with an initial 3D reconstruction as soon as the user completes a single drawing of the desired shape. We complement this single-view network with an updater CNN that refines an existing prediction given a new drawing of the shape created from a novel viewpoint. A key advantage of our approach is that we can apply the updater iteratively to fuse information from an arbitrary number of viewpoints, without requiring explicit stroke correspondences between the drawings. We train both CNNs by rendering synthetic contour drawings from hand-modeled shape collections as well as from procedurally-generated abstract shapes. Finally, we integrate our CNNs in a minimal modeling interface that allows users to seamlessly draw an object, rotate it to see its 3D reconstruction, and refine it by re-drawing from another vantage point using the 3D reconstruction as guidance. The main strengths of our approach are its robustness to freehand bitmap drawings, its ability to adapt to different object categories, and the continuum it offers between single-view and multi-view sketch-based modeling.

Connected through hyperedge HE-1 (induced by source paper "deep sketch based modeling of man made shapes"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "multi chart generative surface modeling"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to infer and execute 3d shape programs"
    - "3dn 3d deformation network"
    - "deep parametric shape predictions using distance fields"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"

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 "sketchzooms deep multi view descriptors for matching line drawings"):
  with members:
    - "neural best buddies sparse cross domain correspondence"
    - "triplet center loss for multi view 3d object retrieval"
    - "sampling matters in deep embedding learning"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "mastering sketching adversarial augmentation for structured prediction"
    - "a neural representation of sketch drawings"
    - "learning aligned cross modal representations from weakly aligned data"
    - "sketch based 3d shape retrieval using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepsketchhair deep sketch based 3d hair modeling"):
  with members:
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "improved training of wasserstein gans"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "photographic image synthesis with cascaded refinement networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "3d shape reconstruction from a single 2d image via 2d 3d self consistency"):
  with members:
    - "matryoshka networks predicting 3d geometry via nested shape layers"
    - "deep single view 3d object reconstruction with visual hull embedding"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning a multi view stereo machine"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "rethinking reprojection closing the loop for pose aware shapereconstruction from a single image"
    - "weakly supervised 3d reconstruction with adversarial constraint"

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)