human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "tuckermpi a parallel c mpi software package for large scale data compression via the tucker tensor decomposition"):
  Abstract: Our goal is compression of massive-scale grid-structured data, such as the multi-terabyte output of a high-fidelity computational simulation. For such data sets, we have developed a new software package called TuckerMPI, a parallel C++/MPI software package for compressing distributed data. The approach is based on treating the data as a tensor, i.e., a multidimensional array, and computing its truncated Tucker decomposition, a higher-order analogue to the truncated singular value decomposition of a matrix. The result is a low-rank approximation of the original tensor-structured data. Compression efficiency is achieved by detecting latent global structure within the data, which we contrast to most compression methods that are focused on local structure. In this work, we describe TuckerMPI, our implementation of the truncated Tucker decomposition, including details of the data distribution and in-memory layouts, the parallel and serial implementations of the key kernels, and analysis of the storage, communication, and computational costs. We test the software on 4.5 terabyte and 6.7 terabyte data sets distributed across 100s of nodes (1000s of MPI processes), achieving compression rates between 100-200,000$\times$ which equates to 99-99.999% compression (depending on the desired accuracy) in substantially less time than it would take to even read the same dataset from a parallel filesystem. Moreover, we show that our method also allows for reconstruction of partial or down-sampled data on a single node, without a parallel computer so long as the reconstructed portion is small enough to fit on a single machine, e.g., in the instance of reconstructing/visualizing a single down-sampled time step or computing summary statistics.

Includes members:
  Member "tthresh tensor compression for multidimensional visual data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low rank tucker approximation of a tensor from streaming data")
  Member "parallel nonnegative cp decomposition of dense tensors":
    also connected through other hyperedges:
      (none)
  Member "scalable tucker factorization for sparse tensors algorithms and discoveries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vest very sparse tucker factorization of large scale tensors")
  Member "on optimizing distributed tucker decomposition for dense tensors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing the linear fascicle evaluation algorithm for multi core and many core systems")
  Member "shared memory parallelization of mttkrp for dense tensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels")
  Member "parallel tensor compression for large scale scientific data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")

Question: Please tell me which class the source paper 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)