human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep vectorization of technical drawings"):
  Abstract: We present a new method for vectorization of technical line drawings, such as floor plans, architectural drawings, and 2D CAD images. Our method includes (1) a deep learning-based cleaning stage to eliminate the background and imperfections in the image and fill in missing parts, (2) a transformer-based network to estimate vector primitives, and (3) optimization procedure to obtain the final primitive configurations. We train the networks on synthetic data, renderings of vector line drawings, and manually vectorized scans of line drawings. Our method quantitatively and qualitatively outperforms a number of existing techniques on a collection of representative technical drawings.

Includes members:
  Member "deepspline data driven reconstruction of parametric curves and surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep sketch based modeling of man made shapes")
  Member "stroke based sketched symbol reconstruction and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can i teach a robot to replicate a line art")
  Member "vectorization of line drawings via polyvector fields":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep parametric shape predictions using distance fields")
  Member "a complete hand drawn sketch vectorization framework":
    also connected through other hyperedges:
      (none)
  Member "vectordefense vectorization as a defense to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding neural networks via feature visualization a survey")
  Member "abc a big cad model dataset for geometric deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "going deeper with point networks")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "mastering sketching adversarial augmentation for structured prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to shade hand drawn sketches")

Question: Please tell me which class the source paper 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)