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 "multi modal 3d shape reconstruction under calibration uncertainty using parametric level set methods"):
  Abstract: We consider the problem of 3D shape reconstruction from multi-modal data, given uncertain calibration parameters. Typically, 3D data modalities can be in diverse forms such as sparse point sets, volumetric slices, 2D photos and so on. To jointly process these data modalities, we exploit a parametric level set method that utilizes ellipsoidal radial basis functions. This method not only allows us to analytically and compactly represent the object, it also confers on us the ability to overcome calibration related noise that originates from inaccurate acquisition parameters. This essentially implicit regularization leads to a highly robust and scalable reconstruction, surpassing other traditional methods. In our results we first demonstrate the ability of the method to compactly represent complex objects. We then show that our reconstruction method is robust both to a small number of measurements and to noise in the acquisition parameters. Finally, we demonstrate our reconstruction abilities from diverse modalities such as volume slices obtained from liquid displacement (similar to CTscans and XRays), and visual measurements obtained from shape silhouettes.

Includes members:
  Member "deep rbfnet point cloud feature learning using radial basis functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep mesh reconstruction from single rgb images via topology modification networks")
  Member "pointnet deep hierarchical feature learning on point sets in a metric space":
    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 "a parametric level set method for partially discrete tomography":
    also connected through other hyperedges:
      (none)
  Member "salt reconstruction in full waveform inversion with a parametric level set method":
    also connected through other hyperedges:
      (none)
  Member "full waveform inversion guided by travel time tomography":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multigrid solver to the helmholtz equation with a point source based on travel time and amplitude")
  Member "jinv a flexible julia package for pde parameter estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems")
  Member "julia a fresh approach to numerical computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "surface reconstruction from scattered point via rbf interpolation on gpu":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving gpu accelerated adaptive idw interpolation algorithm using fast knn search")

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)