human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "matrix multiplication trilinear decompositions apa algorithms and summation"):
  Abstract: Matrix multiplication (hereafter we use the acronym MM) is among the most fundamental operations of modern computations. The efficiency of its performance depends on various factors, in particular vectorization, data movement and arithmetic complexity of the computations, but here we focus just on the study of the arithmetic cost and the impact of this study on other areas of modern computing. In the early 1970s it was expected that the straightforward cubic time algorithm for MM will soon be accelerated to enable MM in nearly quadratic arithmetic time, with some far fetched implications. While pursuing this goal the mainstream research had its focus on the decrease of the classical exponent 3 of the complexity of MM towards its lower bound 2, disregarding the growth of the input size required to support this decrease. Eventually, surprising combinations of novel ideas and sophisticated techniques enabled the decrease of the exponent to its benchmark value of about 2.38, but the supporting MM algorithms improved the straightforward one only for the inputs of immense sizes. Meanwhile, the communication complexity, rather than the arithmetic complexity, has become the bottleneck of computations in linear algebra. This development may seem to undermine the value of the past and future research aimed at the decrease of the arithmetic cost of MM, but we feel that the study should be reassessed rather than closed and forgotten. We review the old and new work in this area in the present day context, recall some major techniques introduced in the study of MM, discuss their impact on the modern theory and practice of computations for MM and beyond MM, and link one of these techniques to some simple algorithms for inner product and summation.

Includes members:
  Member "fast matrix multiplication limitations of the laser method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "barriers for rectangular matrix multiplication")
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "better late than never filling a void in the history of fast matrix multiplication and tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strassen s 2x2 matrix multiplication algorithm a conceptual perspective")
  Member "faster algorithms for rectangular matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic algorithms for decremental approximate shortest paths faster and simpler")
  Member "memory efficient scheduling of strassen winograd s matrix multiplication algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving the space time efficiency of processor oblivious matrix multiplication algorithms")
  Member "all pairs shortest paths using bridging sets and rectangular matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")

Question: Please tell me which class the source paper 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)