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 "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"):
  Abstract: Distributed processing frameworks, such as MapReduce, Hadoop, and Spark are popular systems for processing large amounts of data. The design of efficient algorithms in these frameworks is a challenging problem, as the systems both require parallelism---since datasets are so large that multiple machines are necessary---and limit the degree of parallelism---since the number of machines grows sublinearly in the size of the data. Although MapReduce is over a dozen years old~\cite{dean2008mapreduce}, many fundamental problems, such as Matrix Multiplication, 3-SUM, and All Pairs Shortest Paths, #R##N#lack efficient MapReduce algorithms. We study these problems in the MapReduce setting. Our main contribution is to exhibit smooth trade-offs between the memory available on each machine, and the total number of machines necessary for each problem. Overall, we take the memory available to each machine as a parameter, and aim to minimize the number of rounds and number of machines. #R##N#In this paper, we build on the well-known MapReduce theoretical framework initiated by Karloff, Suri, and Vassilvitskii ~\cite{karloff2010model} and give algorithms for many of these problems. The key to efficient algorithms in this setting lies in defining a sublinear number of large (polynomially sized) subproblems, that can then be solved in parallel. We give strategies for MapReduce-friendly partitioning, that result in new algorithms for all of the above problems. Specifically, we give constant round algorithms for the Orthogonal Vector (OV) and 3-SUM problems, and $O(\log n)$-round algorithms for Matrix Multiplication, All Pairs Shortest Paths (APSP), and Fast Fourier Transform (FFT), among others. In all of these we exhibit trade-offs between the number of machines and memory per machine.

Includes members:
  Member "approximating edit distance in truly subquadratic time quantum and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "random coordinate descent methods for minimizing decomposable submodular functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds")
  Member "powers of tensors and fast matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "parallel algorithms for geometric graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "exact weight subgraphs and the k sum conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "all non trivial variants of 3 ldt are equivalent")
  Member "communication steps for parallel query processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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)