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 general purpose hierarchical mesh partitioning method with node balancing strategies for large scale numerical simulations"
  Abstract: Large-scale parallel numerical simulations are essential for a wide range of engineering problems that involve complex, coupled physical processes interacting across a broad range of spatial and temporal scales. The data structures involved in such simulations (meshes, sparse matrices, etc.) are frequently represented as graphs, and these graphs must be optimally partitioned across the available computational resources in order for the underlying calculations to scale efficiently. Partitions which minimize the number of graph edges that are cut (edge-cuts) while simultaneously maintaining a balance in the amount of work (i.e. graph nodes) assigned to each processor core are desirable, and the performance of most existing partitioning software begins to degrade in this metric for partitions with more than than $O(10^3)$ processor cores. In this work, we consider a general-purpose hierarchical partitioner which takes into account the existence of multiple processor cores and shared memory in a compute node while partitioning a graph into an arbitrary number of subgraphs. We demonstrate that our algorithms significantly improve the preconditioning efficiency and overall performance of realistic numerical simulations running on up to 32,768 processor cores with nearly $10^9$ unknowns.

Connected through hyperedge HE-1 (induced by source paper "moose enabling massively parallel multiphysics simulation"):
  with members:
    - "mfem a modular finite element methods library"
    - "firedrake automating the finite element method by composing abstractions"
    - "best practices for scientific computing"

Connected through hyperedge HE-2 (induced by source paper "parallel memory efficient all at once algorithms for the sparse matrix triple products in multigrid methods"):
  with members:
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

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.MS(Mathematical Software)