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: "i o efficient algorithms for matrix computations"
  Abstract: We analyse some QR decomposition algorithms, and show that the I/O complexity of the tile based algorithm is asymptotically the same as that of matrix multiplication. This algorithm, we show, performs the best when the tile size is chosen so that exactly one tile fits in the main memory. We propose a constant factor improvement, as well as a new recursive cache oblivious algorithm with the same asymptotic I/O complexity. We design Hessenberg, tridiagonal, and bidiagonal reductions that use banded intermediate forms, and perform only asymptotically optimal numbers of I/Os; these are the first I/O optimal algorithms for these problems. In particular, we show that known slab based algorithms for two sided reductions all have suboptimal asymptotic I/O performances, even though they have been reported to do better than the traditional algorithms on the basis of empirical evidence. #R##N#We propose new tile based variants of multishift QR and QZ algorithms that under certain conditions on the number of shifts, have better seek and I/O complexities than all known variants. #R##N#We show that techniques like rescheduling of computational steps, appropriate choosing of the blocking parameters and incorporating of more matrix-matrix operations, can be used to improve the I/O and seek complexities of matrix computations.

Connected through hyperedge HE-1 (induced by source paper "red blue pebbling revisited near optimal parallel matrix matrix multiplication"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "pushing the bounds for matrix matrix multiplication"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"

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.DS(Data Structures and Algorithms)