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: "revisiting matrix product on master worker platforms"
  Abstract: This paper is aimed at designing efficient parallel matrix-product algorithms for heterogeneous master-worker platforms. While matrix-product is well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm and ScaLAPACK outer product algorithm), there are three key hypotheses that render our work original and innovative: #R##N#- Centralized data. We assume that all matrix files originate from, and must be returned to, the master. #R##N#- Heterogeneous star-shaped platforms. We target fully heterogeneous platforms, where computational resources have different computing powers. #R##N#- Limited memory. Because we investigate the parallelization of large problems, we cannot assume that full matrix panels can be stored in the worker memories and re-used for subsequent updates (as in ScaLAPACK). #R##N#We have devised efficient algorithms for resource selection (deciding which workers to enroll) and communication ordering (both for input and result messages), and we report a set of numerical experiments on various platforms at Ecole Normale Superieure de Lyon and the University of Tennessee. However, we point out that in this first version of the report, experiments are limited to homogeneous platforms.

Connected through hyperedge HE-1 (induced by source paper "automated derivation of parametric data movement lower bounds for affine programs"):
  with members:
    - "pushing the bounds for matrix matrix multiplication"
    - "on characterizing the data movement complexity of computational dags for parallel execution"
    - "minimizing communication in linear algebra"
    - "introduction to the ginac framework for symbolic computation within the c programming language"

Connected through hyperedge HE-2 (induced by source paper "the momms family of matrix multiplication algorithms"):
  with members:
    - "a tight i o lower bound for matrix multiplication"
    - "blasfeo basic linear algebra subroutines for embedded optimization"
    - "pushing the bounds for matrix matrix multiplication"

Connected through hyperedge HE-3 (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 for matricized tensor times khatri rao product"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "pushing the bounds for matrix matrix multiplication"):
  with members:
    - "a lower bound technique for communication in bsp"
    - "communication lower bounds for matricized tensor times khatri rao product"
    - "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"

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)