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 generic finite element framework on parallel tree based adaptive meshes"
  Abstract: We present highly scalable parallel distributed-memory algorithms and associated data structures for a generic finite element framework that supports $h$-adaptivity on computational domains represented as multiple connected adaptive trees, thus providing multi-scale resolution on problems governed by partial differential equations. The framework is grounded on a rich representation of the adaptive mesh suitable for generic finite elements that is built on top of a low-level, light-weight forest-of-trees data structure handled by a specialized, highly parallel adaptive meshing engine. Along the way, we have identified the requirements that the forest-of-trees layer must fulfill to be coupled into our framework. Essentially, it must be able to describe neighboring relationships between cells in the adapted mesh (apart from hierarchical relationships) across the lower-dimensional objects at the boundary of the cells. Atop this two-layered mesh representation, we build the rest of data structures required for the numerical integration and assembly of the discrete system of linear equations. We consider algorithms that are suitable for both subassembled and fully-assembled distributed data layouts of linear system matrices. The proposed framework has been implemented within the FEMPAR scientific software library, using p4est as a practical forest-of-octrees demonstrator. A comprehensive strong scaling study of this implementation when applied to Poisson and Maxwell problems reveals remarkable scalability up to 32.2K CPU cores and 482.2M degrees of freedom. Besides, the implementation in FEMPAR of the proposed approach is up to 2.6 and 3.4 times faster than the state-of-the-art deal.ii finite element software in the $h$-adaptive approximation of a Poisson problem with first- and second-order Lagrangian finite elements, respectively (excluding the linear solver step from the comparison).

Connected through hyperedge HE-1 (induced by source paper "a tutorial driven introduction to the parallel finite element library fempar v1 0 0"):
  with members:
    - "scalable solvers for complex electromagnetics problems"
    - "distributed memory parallelization of the aggregated unfitted finite element method"
    - "numerical modelling of heat transfer and experimental validation in powder bed fusion with the virtual domain approximation"
    - "differentiable monotonicity preserving schemes for discontinuous galerkin methods on arbitrary meshes"
    - "simulation of high temperature superconductors and experimental validation"
    - "monotonicity preserving finite element schemes based on differentiable nonlinear stabilization"

Connected through hyperedge HE-2 (induced by source paper "a scalable parallel finite element framework for growing geometries application to metal additive manufacturing"):
  with members:
    - "numerical modelling of heat transfer and experimental validation in powder bed fusion with the virtual domain approximation"
    - "the aggregated unfitted finite element method for elliptic problems"
    - "fempar an object oriented parallel finite element framework"
    - "space time balancing domain decomposition"
    - "morton curve segments produce no more than two distinct face connected subdomains"
    - "recursive algorithms for distributed forests of octrees"
    - "locality and bounding box quality of two dimensional space filling curves"

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)