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: "abc a big cad model dataset for geometric deep learning"
  Abstract: We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows generating data in different formats and resolutions, enabling fair comparisons for a wide range of geometric learning algorithms. As a use case for our dataset, we perform a large-scale benchmark for estimation of surface normals, comparing existing data driven methods and evaluating their performance against both the ground truth and traditional normal estimation methods.

Connected through hyperedge HE-1 (induced by source paper "deep vectorization of technical drawings"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "stroke based sketched symbol reconstruction and segmentation"
    - "vectorization of line drawings via polyvector fields"
    - "a complete hand drawn sketch vectorization framework"
    - "vectordefense vectorization as a defense to adversarial examples"
    - "attention is all you need"
    - "mastering sketching adversarial augmentation for structured prediction"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-2 (induced by source paper "going deeper with point networks"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "p2p net bidirectional point displacement net for shape transform"
    - "multiresolution tree networks for 3d point cloud processing"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"

Connected through hyperedge HE-3 (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"
    - "structure aware generative network for 3d shape modeling"
    - "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-4 (induced by source paper "prs net planar reflective symmetry detection net for 3d models"):
  with members:
    - "robust watertight manifold surface generation method for shapenet models"
    - "learning shape abstractions by assembling volumetric primitives"
    - "learning a predictable and generative vector representation for objects"
    - "thingi10k a dataset of 10 000 3d printing models"
    - "octnet learning deep 3d representations at high resolutions"
    - "shapenet an information rich 3d model repository"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"

Connected through hyperedge HE-5 (induced by source paper "learning to approximate directional fields defined over 2d planes"):
  with members:
    - "procedural synthesis of remote sensing images for robust change detection with neural networks"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "boundary loss for remote sensing imagery semantic segmentation"
    - "vectorization of line drawings via polyvector fields"
    - "targeted change detection in remote sensing images"
    - "perceptual deep depth super resolution"
    - "u net convolutional networks for biomedical image segmentation"

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)