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 deterministic almost tight distributed algorithm for approximating single source shortest paths"):
  Abstract: We present a deterministic $(1+o(1))$-approximation $(n^{1/2+o(1)}+D^{1+o(1)})$-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the CONGEST model); here $n$ is the number of nodes in the network and $D$ is its (hop) diameter. This is the first non-trivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized $(1+o(1))$-approximation $\tilde O(n^{1/2}D^{1/4}+D)$-time algorithm of Nanongkai [STOC 2014] by a factor of as large as $n^{1/8}$, and (ii) the $O(\epsilon^{-1}\log \epsilon^{-1})$-approximation factor of Lenzen and Patt-Shamir's $\tilde O(n^{1/2+\epsilon}+D)$-time algorithm [STOC 2013] within the same running time. Our running time matches the known time lower bound of $\Omega(n^{1/2}/\log n + D)$ [Elkin STOC 2004] up to subpolynomial factors, thus essentially settling the status of this problem which was raised at least a decade ago [Elkin SIGACT News 2004]. It also implies a $(2+o(1))$-approximation $(n^{1/2+o(1)}+D^{1+o(1)})$-time algorithm for approximating a network's weighted diameter which almost matches the lower bound by Holzer and Pinsker [OPODIS 2015]. In achieving this result, we develop two techniques which might be of independent interest and useful in other settings: (i) a deterministic process that replaces the "hitting set argument" commonly used for shortest paths computation in various settings, and (ii) a simple, deterministic, construction of an $(n^{o(1)}, o(1))$-hop set of size $n^{1+o(1)}$. We combine these techniques with many distributed algorithmic techniques, some of which from problems that are not directly related to shortest paths, e.g., ruling sets [Goldberg et al. STOC 1987], source detection [Lenzen and Peleg PODC 2013], and partial distance estimation [Lenzen and Patt-Shamir PODC 2015].

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 "decremental single source shortest paths on undirected graphs in near linear total update time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "approximation of distances and shortest paths in the broadcast congest clique":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "fast partial distance estimation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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-6 (induced by source paper "distributed edge connectivity in sublinear time")
  Member "distributed minimum cut approximation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "tight bounds for distributed minimum weight spanning tree verification":
    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)