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 mst a smoothed analysis"):
  Abstract: We study smoothed analysis of distributed graph algorithms, focusing on the fundamental minimum spanning tree (MST) problem. With the goal of studying the time complexity of distributed MST as a function of the "perturbation" of the input graph, we posit a {\em smoothing model} that is parameterized by a smoothing parameter $0 \leq \epsilon(n) \leq 1$ which controls the amount of {\em random} edges that can be added to an input graph $G$ per round. Informally, $\epsilon(n)$ is the probability (typically a small function of $n$, e.g., $n^{-\frac{1}{4}}$) that a random edge can be added to a node per round. The added random edges, once they are added, can be used (only) for communication. #R##N#We show upper and lower bounds on the time complexity of distributed MST in the above smoothing model. We present a distributed algorithm that, with high probability,\footnote{Throughout, with high probability (whp) means with probability at least $1 - n^{-c}$, for some fixed, positive constant $c$.} computes an MST and runs in $\tilde{O}(\min\{\frac{1}{\sqrt{\epsilon(n)}} 2^{O(\sqrt{\log n})}, D + \sqrt{n}\})$ rounds\footnote{The notation $\tilde{O}$ hides a $\polylog(n)$ factor and $\tilde{\Omega}$ hides a $\frac{1}{\polylog{(n)}}$ factor, where $n$ is the number of nodes of the graph.} where $\epsilon$ is the smoothing parameter, $D$ is the network diameter and $n$ is the network size. To complement our upper bound, we also show a lower bound of $\tilde{\Omega}(\min\{\frac{1}{\sqrt{\epsilon(n)}}, D+\sqrt{n}\})$. We note that the upper and lower bounds essentially match except for a multiplicative $2^{O(\sqrt{\log n})} \polylog(n)$ factor. #R##N#Our work can be considered as a first step in understanding the smoothed complexity of distributed graph algorithms.

Includes members:
  Member "fast approximate shortest paths 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 "fast and efficient distributed computation of hamiltonian cycles in random graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hardness of distributed optimization")
  Member "congested clique algorithms for graph spanners":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "massively parallel algorithms for distance approximation and spanners")
  Member "new distributed algorithms in almost mixing time via transformations from parallel algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "near optimal schedules for simultaneous multicasts")
  Member "distributed symmetry breaking algorithms for congested cliques":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "simple graph coloring algorithms for congested clique and massively parallel computation")
  Member "a simple deterministic distributed mst algorithm with near optimal time and message complexities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved algorithm for min cuts in distributed networks")
  Member "towards a complexity theory for the congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "a time and message optimal distributed algorithm for minimum spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved algorithm for min cuts in distributed networks")

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)