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: "communication lower bounds for matricized tensor times khatri rao product"
  Abstract: The matricized-tensor times Khatri-Rao product computation is the typical bottleneck in algorithms for computing a CP decomposition of a tensor. In order to develop high performance sequential and parallel algorithms, we establish communication lower bounds that identify how much data movement is required for this computation in the case of dense tensors. We also present sequential and parallel algorithms that attain the lower bounds and are therefore communication optimal. In particular, we show that the structure of the computation allows for less communication than the straightforward approach of casting the computation as a matrix multiplication operation.

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"
    - "shared memory parallelization of mttkrp for dense tensors"
    - "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 "a tight i o lower bound for matrix multiplication"):
  with members:
    - "communication optimal convolutional neural nets"
    - "a lower bound technique for communication in bsp"
    - "communication lower bounds of bilinear algorithms for symmetric tensor contractions"
    - "communication lower bounds for distributed memory computations"
    - "communication lower bounds for distributed memory computations"
    - "minimizing communication in linear algebra"
    - "revisiting matrix product on master worker platforms"

Connected through hyperedge HE-3 (induced by source paper "pushing the bounds for matrix matrix multiplication"):
  with members:
    - "a lower bound technique for communication in bsp"
    - "communication lower bounds of bilinear algorithms for symmetric tensor contractions"
    - "communication lower bounds for distributed memory computations"
    - "communication lower bounds for distributed memory computations"
    - "minimizing communication in linear algebra"
    - "revisiting matrix product on master worker platforms"

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)