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 "improved distributed approximation to maximum independent set"):
  Abstract: We present improved results for approximating Maximum Independent Set ($\MaxIS$) in the standard LOCAL and CONGEST models of distributed computing. Let $n$ and $\Delta$ be the number of nodes and maximum degree in the input graph, respectively. Bar-Yehuda et al. [PODC 2017] showed that there is an algorithm in the CONGEST model that finds a $\Delta$-approximation to $\MaxIS$ in $O(\MIS(n,\Delta)\log W)$ rounds, where $\MIS(n,\Delta)$ is the running time for finding a \emph{maximal} independent set, and $W$ is the maximum weight of a node in the network. Whether their algorithm is randomized or deterministic depends on the $\MIS$ algorithm that they use as a black-box. Our results: (1) A deterministic $O(\MIS(n,\Delta))$ rounds algorithm for $O(\Delta)$-approximation to $\MaxIS$ in the CONGEST model. (2) A randomized $2^{O(\sqrt{\log \log n})}$ rounds algorithm that finds, with high probability, an $O(\Delta)$-approximation to $\MaxIS$ in the CONGEST model. (3) An $\Omega(\log^*n)$ lower bound for any randomized algorithm that finds an independent set of size $\Omega(n/\Delta)$ that succeeds with probability at least $1-1/\log n$, even for the LOCAL model. This hardness result applies for graphs of maximum degree $\Delta=O(n/\log^*n)$. One might wonder whether the same hardness result applies for low degree graphs. We rule out this possibility with our next result. (4) An $O(1)$ rounds algorithm that finds an independent set of size $\Omega(n/\Delta)$ in graphs with maximum degree $\Delta\leq n/\log n$, with high probability. Due to a lower bound of $\Omega(\sqrt{\log n/\log \log n})$ that was given by Kuhn, Moscibroda and Wattenhofer [JACM, 2016] on the number of rounds for finding a maximal independent set ($\MIS$) in the LOCAL model, even for randomized algorithms, our second result implies that finding an $O(\Delta)$-approximation to $\MaxIS$ is strictly easier than $\MIS$.

Includes members:
  Member "polylogarithmic time deterministic network decomposition and distributed derandomization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "massively parallel algorithms for distance approximation and spanners")
  Member "lower bounds for maximal matchings and maximal independent sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed maximum matching verification in congest")
  Member "simple and local independent set approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hardness of distributed optimization")
  Member "deterministic distributed edge coloring via hypergraph maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "quadratic and near quadratic lower bounds for the congest model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "distributed approximation of maximum independent set and maximum matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed maximum matching verification in congest")
  Member "derandomizing local distributed algorithms under bandwidth restrictions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")

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)