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: "a scalable parallel finite element framework for growing geometries application to metal additive manufacturing"
  Abstract: This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely been the focus in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the geometry growth; (3) the customization of a parallel iterative linear solver, which leverages the so-called balancing domain decomposition by constraints preconditioning approach for fast convergence and high parallel scalability. Computational experiments consider the part-scale thermal analysis of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong scaling analysis is carried out for a simulation of 48 layers printed in a cuboid. The cuboid is adaptively meshed to model a layer-by-layer metal deposition process and the average global problem size amounts to 10.3 million unknowns. An unprecedented scalability for problems with growing domains is achieved, with the capability of simulating the printing and recoat of a single layer in 8 seconds average on 3,072 processors. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem with growing-in-time geometries.

Connected through hyperedge HE-1 (induced by source paper "massively parallel finite difference elasticity using a block structured adaptive mesh refinement with a geometric multigrid solver"):
  with members:
    - "a survey of high level frameworks in block structured adaptive mesh refinement packages"

Connected through hyperedge HE-2 (induced by source paper "parametric shape optimization for combined additive subtractive manufacturing"):
  with members:
    - "numerical modelling of heat transfer and experimental validation in powder bed fusion with the virtual domain approximation"

Connected through hyperedge HE-3 (induced by source paper "numerical modelling of heat transfer and experimental validation in powder bed fusion with the virtual domain approximation"):
  with members:
    - "fempar an object oriented parallel finite element framework"

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)