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: "vectorization of line drawings via polyvector fields"
  Abstract: Image tracing is a foundational component of the workflow in graphic design, engineering, and computer animation, linking hand-drawn concept images to collections of smooth curves needed for geometry processing and editing. Even for clean line drawings, modern algorithms often fail to faithfully vectorize junctions, or points at which curves meet; this produces vector drawings with incorrect connectivity. This subtle issue undermines the practical application of vectorization tools and accounts for hesitance among artists and engineers to use automatic vectorization software. To address this issue, we propose a novel image vectorization method based on state-of-the-art mathematical algorithms for frame field processing. Our algorithm is tailored specifically to disambiguate junctions without sacrificing quality.

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"
    - "a complete hand drawn sketch vectorization framework"
    - "vectordefense vectorization as a defense to adversarial examples"
    - "abc a big cad model dataset for geometric deep learning"
    - "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 "deep parametric shape predictions using distance fields"):
  with members:
    - "deepspline data driven reconstruction of parametric curves and surfaces"
    - "learning shape templates with structured implicit functions"
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-3 (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"
    - "targeted change detection in remote sensing images"
    - "perceptual deep depth super resolution"
    - "abc a big cad model dataset for geometric deep learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a complete hand drawn sketch vectorization framework"):
  with members:
    - "mastering sketching adversarial augmentation for structured prediction"

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)