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 "faster distributed shortest path approximations via shortcuts"):
  Abstract: A long series of recent results and breakthroughs have led to faster and better distributed approximation algorithms for single source shortest paths (SSSP) and related problems in the CONGEST model. The runtime of all these algorithms, however, is $\tilde{\Omega}(\sqrt{n})$, regardless of the network topology, even on nice networks with a (poly)logarithmic network diameter $D$. While this is known to be necessary for some pathological networks, most topologies of interest are arguably not of this type. #R##N#We give the first distributed approximation algorithms for shortest paths problems that adjust to the topology they are run on, thus achieving significantly faster running times on many topologies of interest. The running time of our algorithms depends on and is close to $Q$, where $Q$ is the quality of the best shortcut that exists for the given topology. While $Q = \tilde{\Theta}(\sqrt{n} + D)$ for pathological worst-case topologies, many topologies of interest have $Q = \tilde{\Theta}(D)$, which results in near instance optimal running times for our algorithm, given the trivial $\Omega(D)$ lower bound. #R##N#The problems we consider are as follows: (1) an approximate shortest path tree and SSSP distances, (2) a polylogarithmic size distance label for every node such that from the labels of any two nodes alone one can determine their distance (approximately), and (3) an (approximately) optimal flow for the transshipment problem. #R##N#Our algorithms have a tunable tradeoff between running time and approximation ratio. Our fastest algorithms have an arbitrarily good polynomial approximation guarantee and an essentially optimal $\tilde{O}(Q)$ running time. On the other end of the spectrum, we achieve polylogarithmic approximations in $\tilde{O}(Q \cdot n^{\epsilon})$ rounds for any $\epsilon > 0$. It seems likely that eventually, our non-trivial approximation algorithms for the...

Includes members:
  Member "near optimal approximate shortest paths and transshipment in distributed and streaming models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "low congestion shortcuts without embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal schedules for simultaneous multicasts")
  Member "distributed strong diameter network decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the use of randomness in local distributed graph algorithms")
  Member "an almost tight distributed algorithm for computing single source shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel approximate undirected shortest paths via low hop emulators")
  Member "fast partial distance estimation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast approximate shortest paths in the congested clique")
  Member "almost tight distributed minimum cut algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improved algorithm for min cuts in distributed networks")
  Member "distributed approximation algorithms for weighted shortest paths":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "cops robbers and threatening skeletons padded decomposition for minor free graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scattering and sparse partitions and their applications")

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)