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 "lessons from the congested clique applied to mapreduce"):
  Abstract: The main results of this paper are (I) a simulation algorithm which, under quite general constraints, transforms algorithms running on the Congested Clique into algorithms running in the MapReduce model, and (II) a distributed $O(\Delta)$-coloring algorithm running on the Congested Clique which has an expected running time of (i) $O(1)$ rounds, if $\Delta \geq \Theta(\log^4 n)$; and (ii) $O(\log \log n)$ rounds otherwise. Applying the simulation theorem to the Congested-Clique $O(\Delta)$-coloring algorithm yields an $O(1)$-round $O(\Delta)$-coloring algorithm in the MapReduce model. #R##N#Our simulation algorithm illustrates a natural correspondence between per-node bandwidth in the Congested Clique model and memory per machine in the MapReduce model. In the Congested Clique (and more generally, any network in the $\mathcal{CONGEST}$ model), the major impediment to constructing fast algorithms is the $O(\log n)$ restriction on message sizes. Similarly, in the MapReduce model, the combined restrictions on memory per machine and total system memory have a dominant effect on algorithm design. In showing a fairly general simulation algorithm, we highlight the similarities and differences between these models.

Includes members:
  Member "algebraic methods in the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "approximation of distances and shortest paths in the broadcast congest clique":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "distributed approximation algorithms for weighted shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "near constant time distributed algorithms on a congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed approximation algorithms for steiner tree in the mathcal congested mathcal clique")
  Member "a super fast distributed algorithm for bipartite metric facility location":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "near constant time distributed algorithms on a congested clique")
  Member "super fast distributed algorithms for metric facility location":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed approximation algorithms for steiner tree in the mathcal congested mathcal clique")
  Member "optimal deterministic routing and sorting on the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "tri tri again finding triangles and small subgraphs in a distributed setting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel algorithms for small subgraph counting")

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)