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: "shared memory parallelization of mttkrp for dense tensors"
  Abstract: The matricized-tensor times Khatri-Rao product (MTTKRP) is the computational bottleneck for algorithms computing CP decompositions of tensors. In this paper, we develop shared-memory parallel algorithms for MTTKRP involving dense tensors. The algorithms cast nearly all of the computation as matrix operations in order to use optimized BLAS subroutines, and they avoid reordering tensor entries in memory. We benchmark sequential and parallel performance of our implementations, demonstrating high sequential performance and efficient parallel scaling. We use our parallel implementation to compute a CP decomposition of a neuroimaging data set and achieve a speedup of up to $7.4\times$ over existing parallel software.

Connected through hyperedge HE-1 (induced by source paper "enabling distributed memory tensor completion in python using new sparse tensor kernels"):
  with members:
    - "pasta a parallel sparse tensor algorithm benchmark suite"
    - "format abstraction for sparse tensor algebracompilers"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "hptt a high performance tensor transposition c library"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "tensorflow a system for large scale machine learning"
    - "sparse tensor algebra as a parallel programming model"

Connected through hyperedge HE-2 (induced by source paper "tuckermpi a parallel c mpi software package for large scale data compression via the tucker tensor decomposition"):
  with members:
    - "tthresh tensor compression for multidimensional visual data"
    - "parallel nonnegative cp decomposition of dense tensors"
    - "scalable tucker factorization for sparse tensors algorithms and discoveries"
    - "on optimizing distributed tucker decomposition for dense tensors"
    - "parallel tensor compression for large scale scientific data"

Connected through hyperedge HE-3 (induced by source paper "parallel nonnegative cp decomposition of dense tensors"):
  with members:
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "communication lower bounds for matricized tensor times khatri rao product"):
  with members:
    - "pushing the bounds for matrix matrix multiplication"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "parallel tensor compression for large scale scientific data"
    - "tensor decompositions for learning latent variable models"

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.DC(Distributed, Parallel, and Cluster Computing)