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 "sparse matrix multiplication and triangle listing in the congested clique model"):
  Abstract: We multiply two $n \times n$ matrices $S,T$ over semirings in the Congested Clique model, where $n$ fully connected nodes communicate synchronously using $O(\log n)$-bit messages, within $O(nz(S)^{1/3} nz(T)^{1/3}/n + 1)$ rounds of communication, where $nz(A)$ denotes the number of non-zero elements in a matrix $A$. By leveraging the sparsity of the input matrices, our algorithm greatly reduces communication compared with general algorithms [Censor-Hillel et al., PODC 2015], improving upon the state-of-the-art for matrices with $o(n^2)$ non-zero elements. Our algorithm exhibits the additional strength of surpassing previous solutions also when only one matrix is sparse. This allows efficiently raising a sparse matrix to a power greater than 2. As applications, we speed up 4-cycle counting and APSP in sparse graphs. #R##N#Our algorithmic contribution is a new \emph{deterministic} method of restructuring the input matrices in a sparsity-aware manner, which assigns each node with element-wise multiplication tasks that are not necessarily consecutive but are balanced, yielding communication-efficient multiplication. #R##N#Moreover, this new deterministic method for restructuring matrices may be used to restructure the adjacency matrix of input graphs, enabling faster solutions for graph related problems. As an example, we present a new deterministic algorithm which solves the triangle listing problem in $O(m/n^{5/3} + 1)$ rounds, a complexity that was previously obtained by a \emph{randomized} algorithm [Pandurangan et al., SPAA 2018] and matches the lower bound of $\tilde{\Omega}(n^{1/3})$ when $m=n^2$ of [Izumi and Le Gall, PODC 2017, Pandurangan et al., SPAA 2018]. #R##N#Our triangle listing algorithm implies triangle counting with the same complexity of $O(m/n^{5/3} + 1)$ rounds, which is a \emph{cubic} improvement over the previous $O(m^2/n^3)$-round algorithm [Dolev et al., DISC 2012].

Includes members:
  Member "multi threaded sparse matrix matrix multiplication for many core and gpu architectures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "red blue pebbling revisited near optimal parallel matrix matrix multiplication")
  Member "triangle finding and listing in congest networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed testing of graph isomorphism in the congest model")
  Member "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor":
    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 "on the distributed complexity of large scale graph computations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "further algebraic algorithms in the congested clique model and applications to graph theoretic problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "hypergraph partitioning for sparse matrix matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "scaling betweenness centrality using communication efficient sparse matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a unified iteration space transformation framework for sparse and dense tensor algebra")

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)