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 "massively parallel symmetry breaking on sparse graphs mis and maximal matching"):
  Abstract: The success of massively parallel computation (MPC) paradigms such as MapReduce has led to a significant interest in better understanding their true computational power. The fundamental question in this regard is how the advantages of this model (e.g. free local computation) can be leveraged to improve the round-complexity of algorithms inherited from traditional parallel and distributed models such as PRAM or LOCAL. Problems such as maximal independent set (MIS) or maximal matching are among the most intensively studied problems in the field and logarithmic round algorithms have been known for these problems from 1980s. However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of $n^{1-\Omega(1)}$ per machine where $n$ denotes the number of vertices. #R##N#Our main result is a truly sublinear algorithm that takes $O(\log \alpha + \log^2\log n)$ rounds to compute MIS or maximal matching using an optimal total space of $\tilde{O}(m)$ where $m$ denotes the number of edges and $\alpha$ denotes the arboricity of the input graph. We believe parametrization by arboricity is particularly interesting for this regime of MPC since most families of sparse graphs have a small arboricity. Our algorithms do not assume arboricity is constant and do not require to be given $\alpha$. This is the first substantial improvement over the known PRAM/LOCAL algorithms for these problems on such a wide class of graphs. #R##N#Since trees have arboricity one, our algorithm improves and generalizes the recent algorithm of Brandt et al.~[arXiv:1802.06748] that finds MIS on trees in $O(\log^3\log n)$ rounds. Moreover, the $n$-dependency of our algorithm exponentially improves over the corresponding $O(\log \alpha + \sqrt{\log n})$ PRAM/LOCAL bounds by Barenboim et al.~[FOCS'12] and Ghaffari~[SODA'16].

Includes members:
  Member "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "greedy and local ratio algorithms in the mapreduce model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "mst in o 1 rounds of the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how fast can you update your mst dynamic algorithms for cluster computing")
  Member "improved deterministic distributed matching via rounding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "round compression for parallel matching algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "on estimating maximum matching size in graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "the sparse awakens streaming algorithms for matching size estimation in sparse graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")

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.DC(Distributed, Parallel, and Cluster Computing)