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: "deepspline data driven reconstruction of parametric curves and surfaces"
  Abstract: Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics. Optimal implementations of these applications have traditionally involved the use of spline-based representations at their core. Most such methods attempt to solve optimization problems that minimize an output-target mismatch. However, these optimization techniques require an initialization that is close enough, as they are local methods by nature. We propose a deep learning architecture that adapts to perform spline fitting tasks accordingly, providing complementary results to the aforementioned traditional methods. We showcase the performance of our approach, by reconstructing spline curves and surfaces based on input images or point clouds.

Connected through hyperedge HE-1 (induced by source paper "deep vectorization of technical drawings"):
  with members:
    - "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"
    - "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 sketch based modeling of man made shapes"):
  with members:
    - "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"
    - "learning category specific mesh reconstruction from image collections"

Connected through hyperedge HE-3 (induced by source paper "deep parametric shape predictions using distance fields"):
  with members:
    - "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"
    - "vectorization of line drawings via polyvector fields"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "learning 3d shape completion under weak supervision"

Connected through hyperedge HE-4 (induced by source paper "neural shape parsers for constructive solid geometry"):
  with members:
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "learning to infer and execute 3d shape programs"
    - "superquadrics revisited learning 3d shape parsing beyond cuboids"
    - "3d prnn generating shape primitives with recurrent neural networks"
    - "inferring and executing programs for visual reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "csgnet neural shape parser for constructive solid geometry"
    - "learning to infer graphics programs from hand drawn images"

Connected through hyperedge HE-5 (induced by source paper "fast interactive object annotation with curve gcn"):
  with members:
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "path aggregation network for instance segmentation"
    - "geometric deep learning going beyond euclidean data"
    - "annotating object instances with a polygon rnn"
    - "neural 3d mesh renderer"
    - "mask r cnn"

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.CV(Computer Vision and Pattern Recognition)