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 "randomization of approximate bilinear computation for matrix multiplication"):
  Abstract: We present a method for randomizing a formula for bilinear computation of matrix products. We consider the implications of such randomization when the formula itself is approximate, and when the formula is exact but its computation is plagued by numerical error due to finite precision arithmetic. Our theoretical results and numerical experiments indicate that our method can improve performance in both settings for a negligible increase in computational complexity.

Includes members:
  Member "training deep neural networks with 8 bit floating point numbers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse weight activation training")
  Member "implementing strassen s algorithm with cutlass on nvidia volta gpus":
    also connected through other hyperedges:
      (none)
  Member "improving the numerical stability of fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalent polyadic decompositions of matrix multiplication tensors")
  Member "deep learning with limited numerical precision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  Member "a framework for practical parallel fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deterministic combinatorial replacement paths and distance sensitivity oracles")
  Member "compressed matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solving tall dense linear programs in nearly linear time")

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)