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 "distributed computation of large scale graph problems"):
  Abstract: Motivated by the increasing need for fast distributed processing of large-scale graphs such as the Web graph and various social networks, we study a message-passing distributed computing model for graph processing and present lower bounds and algorithms for several graph problems. This work is inspired by recent large-scale graph processing systems (e.g., Pregel and Giraph) which are designed based on the message-passing model of distributed computing. #R##N#Our model consists of a point-to-point communication network of $k$ machines interconnected by bandwidth-restricted links. Communicating data between the machines is the costly operation (as opposed to local computation). The network is used to process an arbitrary $n$-node input graph (typically $n \gg k > 1$) that is randomly partitioned among the $k$ machines (a common implementation in many real world systems). Our goal is to study fundamental complexity bounds for solving graph problems in this model. #R##N#We present techniques for obtaining lower bounds on the distributed time complexity. Our lower bounds develop and use new bounds in random-partition communication complexity. We first show a lower bound of $\Omega(n/k)$ rounds for computing a spanning tree (ST) of the input graph. This result also implies the same bound for other fundamental problems such as computing a minimum spanning tree (MST). We also show an $\Omega(n/k^2)$ lower bound for connectivity, ST verification and other related problems. #R##N#We give algorithms for various fundamental graph problems in our model. We show that problems such as PageRank, MST, connectivity, and graph covering can be solved in $\tilde{O}(n/k)$ time, whereas for shortest paths, we present algorithms that run in $\tilde{O}(n/\sqrt{k})$ time (for $(1+\epsilon)$-factor approx.) and in $\tilde{O}(n/k)$ time (for $O(\log n)$-factor approx.) respectively.

Includes members:
  Member "fast distributed pagerank computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "walking randomly massively and efficiently")
  Member "distributed approximation algorithms for weighted shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "when distributed computation is communication expensive":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the communication complexity of optimization")
  Member "can quantum communication speed up distributed computation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "streaming balanced graph partitioning for random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph partitioning via parallel submodular approximation to accelerate distributed machine learning")
  Member "tri tri again finding triangles and small subgraphs in a distributed setting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "sublinear bounds for randomized leader election":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "message reduction in the local model is a free lunch")
  Member "dense subgraphs on dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed edge connectivity in sublinear 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.DC(Distributed, Parallel, and Cluster Computing)