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 sketch based modeling of man made shapes"):
  Abstract: Sketch-based modeling aims to model 3D geometry using a concise and easy to create---but extremely ambiguous---input: artist sketches. Most conventional sketch-based modeling systems target smooth shapes and, to counter the ambiguity, put manually-designed priors on the 3D shape; they also typically require clean, vectorized input. Recent approaches attempt to learn those priors from data but often produce low-quality output. Focusing on piecewise-smooth man-made shapes, we address these issues by presenting a deep learning-based system to infer a complete man-made 3D shape from a single bitmap sketch. Given a sketch, our system infers a set of parametric surfaces that realize the drawing in 3D. To capture the piecewise smooth geometry of man-made shapes, we learn a special shape representation---a deformable parametric template composed of Coons patches. Naively training such a system, however, would suffer from lack of data and from self-intersections of the parametric surfaces. To address this, we introduce a synthetic sketch augmentation pipeline as well as a loss function that biases the network to output non-self-intersecting shapes. We demonstrate the efficacy of our system on a gallery of synthetic and real artist sketches as well as via comparison to related work.

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 vectorization of technical drawings")
  Member "learning shape templates with structured implicit functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "multi chart generative surface modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "deepsdf learning continuous signed distance functions for shape representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep geometry learning from a representation perspective")
  Member "learning to infer and execute 3d shape programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")
  Member "3dn 3d deformation network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "deep parametric shape predictions using distance fields":
    also connected through other hyperedges:
      (none)
  Member "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pt2pc learning to generate 3d point cloud shapes from part tree conditions")

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.GR(Graphics)