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 "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation"):
  Abstract: We present a distributed solver for a large and important class of Laplacian systems that we call "one-sink" Laplacian systems. Specifically, our solver can produce solutions for systems of the form $L\mathbf{x} = \mathbf{b}$ where exactly one of the coordinates of $\mathbf{b}$ is negative. Our solver is an organically distributed algorithm that takes $\widetilde{O}(n/\lambda_2^L)$ rounds for bounded degree graphs to produce an approximate solution where $\lambda_2^L$ is the second smallest eigenvalue of the Laplacian matrix of graph, also known as the Fiedler value or algebraic connectivity of graph. The class of one-sink Laplacians includes the important voltage computation problem and allows us to compute the effective resistance between nodes in a distributed manner. As a result, our Laplacian solver can be used to adapt the approach by Kelner and Mądry (2009) to give the first distributed algorithm to compute approximate random spanning trees efficiently. #R##N#Our solver, which we call "Distributed Random Walk-based Laplacian Solver" (DRW-LSolve) works by approximating the stationary distribution of a multi-dimensional Markov chain. This chain describes the evolution of a "data collection" process where each node $v$ for which $\mathbf{b}_v > 0$ generates data packets with a rate proportional to $\mathbf{b}_v$ and the node for which $\mathbf{b}_v < 0$ acts as a sink. The nodes of the graph relay the packets, staging them in their queues and transmitting one at a time. We show that when this multidimensional chain is ergodic the vector whose $v$th coordinate is proportional to the probability at stationarity of the queue at $v$ being non-empty is a solution to $L\mathbf{x} = \mathbf{b}$.

Includes members:
  Member "current flow group closeness centrality for complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group centrality maximization for large scale graphs")
  Member "pooling or sampling collective dynamics for electrical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "an almost linear time algorithm for uniform random spanning tree generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "decentralized random walk based data collection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random walk based in network computation of arbitrary functions")
  Member "sampling random spanning trees faster than matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "estimating current flow closeness centrality with a multigrid laplacian solver":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "current flow group closeness centrality for complex networks")
  Member "computing maximum flow with augmenting electrical flows":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "a fast distributed solver for symmetric diagonally dominant linear equations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "caddelag framework for distributed anomaly detection in large dense graph sequences")

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)