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: "task based algorithm for matrix multiplication a step towards block sparse tensor computing"
  Abstract: Distributed-memory matrix multiplication (MM) is a key element of algorithms in many domains (machine learning, quantum physics). Conventional algorithms for dense MM rely on regular/uniform data decomposition to ensure load balance. These traits conflict with the irregular structure (block-sparse or rank-sparse within blocks) that is increasingly relevant for fast methods in quantum physics. To deal with such irregular data we present a new MM algorithm based on Scalable Universal Matrix Multiplication Algorithm (SUMMA). The novel features are: (1) multiple-issue scheduling of SUMMA iterations, and (2) fine-grained task-based formulation. The latter eliminates the need for explicit internodal synchronization; with multiple-iteration scheduling this allows load imbalance due to nonuniform matrix structure. For square MM with uniform and nonuniform block sizes (the latter simulates matrices with general irregular structure) we found excellent performance in weak and strong-scaling regimes, on commodity and high-end hardware.

Connected through hyperedge HE-1 (induced by source paper "high performance tensor contraction without transposition"):
  with members:
    - "ttc a high performance compiler for tensor transpositions"
    - "design of a high performance gemm like tensor tensor multiplication"
    - "scalable task based algorithm for multiplication of block rank sparse matrices"
    - "on the performance prediction of blas based tensor contractions"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "the numpy array a structure for efficient numerical computation"

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)