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: "surface reconstruction from scattered point via rbf interpolation on gpu"
  Abstract: In this paper we describe a parallel implicit method based on radial basis functions (RBF) for surface reconstruction. The applicability of RBF methods is hindered by its computational demand, that requires the solution of linear systems of size equal to the number of data points. Our reconstruction implementation relies on parallel scientific libraries and is supported for massively multi-core architectures, namely Graphic Processor Units (GPUs). The performance of the proposed method in terms of accuracy of the reconstruction and computing time shows that the RBF interpolant can be very effective for such problem.

Connected through hyperedge HE-1 (induced by source paper "multi modal 3d shape reconstruction under calibration uncertainty using parametric level set methods"):
  with members:
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "a parametric level set method for partially discrete tomography"
    - "salt reconstruction in full waveform inversion with a parametric level set method"
    - "full waveform inversion guided by travel time tomography"
    - "jinv a flexible julia package for pde parameter estimation"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-2 (induced by source paper "improving gpu accelerated adaptive idw interpolation algorithm using fast knn search"):
  with members:
    - "accelerating adaptive inverse distance weighting interpolation algorithm on a graphics processing unit"
    - "impact of data layouts on the efficiency of gpu accelerated idw interpolation"
    - "fast k nng construction with gpu based quick multi select"
    - "fast k nearest neighbor search using gpu"

Connected through hyperedge HE-3 (induced by source paper "an error estimate of gaussian recursive filter in 3dvar problem"):
  with members:
    - "a revised scheme to compute horizontal covariances in an oceanographic 3d var assimilation system"

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.DC(Distributed, Parallel, and Cluster Computing)