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: "full waveform inversion guided by travel time tomography"
  Abstract: Full waveform inversion (FWI) is a process in which seismic numerical simulations are fit to observed data by changing the wave velocity model of the medium under investigation. The problem is non-linear, and therefore optimization techniques have been used to find a reasonable solution to the problem. The main problem in fitting the data is the lack of low spatial frequencies. This deficiency often leads to a local minimum and to non-plausible solutions. In this work we explore how to obtain low frequency information for FWI. Our approach involves augmenting FWI with travel time tomography, which has low-frequency features. By jointly inverting these two problems we enrich FWI with information that can replace low frequency data. In addition, we use high order regularization, in a preliminary inversion stage, to prevent high frequency features from polluting our model in the initial stages of the reconstruction. This regularization also promotes the non-dominant low-frequency modes that exist in the FWI sensitivity. By applying a joint FWI and travel time inversion we are able to obtain a smooth model than can later be used to recover a good approximation for the true model. A second contribution of this paper involves the acceleration of the main computational bottleneck in FWI--the solution of the Helmholtz equation. We show that the solution time can be reduced by solving the equation for multiple right hand sides using block multigrid preconditioned Krylov methods.

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"
    - "jinv a flexible julia package for pde parameter estimation"
    - "julia a fresh approach to numerical computing"
    - "surface reconstruction from scattered point via rbf interpolation on gpu"

Connected through hyperedge HE-2 (induced by source paper "a multigrid solver to the helmholtz equation with a point source based on travel time and amplitude"):
  with members:
    - "complex additive geometric multilevel solvers for helmholtz equations on spacetrees"
    - "jinv a flexible julia package for pde parameter estimation"
    - "a fast marching algorithm for the factored eikonal equation"
    - "julia a fresh approach to numerical computing"
    - "a parallel sweeping preconditioner for heterogeneous 3d helmholtz equations"

Connected through hyperedge HE-3 (induced by source paper "jinv a flexible julia package for pde parameter estimation"):
  with members:
    - "a fast marching algorithm for the factored eikonal equation"
    - "a framework for automated pde constrained optimisation"
    - "julia a fast dynamic language for technical computing"

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.CE(Computational Engineering, Finance, and Science)