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 flexible framework for parallel multi dimensional dfts"
  Abstract: Multi-dimensional discrete Fourier transforms (DFT) are typically decomposed into multiple 1D transforms. Hence, parallel implementations of any multi-dimensional DFT focus on parallelizing within or across the 1D DFT. Existing DFT packages exploit the inherent parallelism across the 1D DFTs and offer rigid frameworks, that cannot be extended to incorporate both forms of parallelism and various data layouts to enable some of the parallelism. However, in the era of exascale, where systems have thousand of nodes and intricate network topologies, flexibility and parallel efficiency are key aspects all multi-dimensional DFT frameworks need to have in order to map and scale the computation appropriately. In this work, we present a flexible framework, built on the Redistribution Operations and Tensor Expressions (ROTE) framework, that facilitates the development of a family of parallel multi-dimensional DFT algorithms by 1) unifying the two parallelization schemes within a single framework, 2) exploiting the two different parallelization schemes to different degrees and 3) using different data layouts to distribute the data across the compute nodes. We demonstrate the need of a versatile framework and thus a need for a family of parallel multi-dimensional DFT algorithms on the K-Computer, where we show almost linear strong scaling results for problem sizes of 1024^3 on 32k compute nodes.

Connected through hyperedge HE-1 (induced by source paper "large scale discrete fourier transform on tpus"):
  with members:
    - "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "high performance monte carlo simulation of ising model on tpu clusters"
    - "tensor processing units for financial monte carlo"
    - "in datacenter performance analysis of a tensor processing unit"
    - "a berkeley view of systems challenges for ai"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "p3dfft a framework for parallel computations of fourier transforms in three dimensions"

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)