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 approximation algorithms for maximum matching in graphs and hypergraphs"):
  Abstract: We describe randomized and deterministic approximation algorithms in Linial's classic LOCAL model of distributed computing to find maximum-weight matchings in hypergraphs. For a rank-$r$ hypergraph, our algorithm generates a matching within an $O(r)$ factor of the maximum weight matching. The runtime is $\tilde O(\log r \log \Delta)$ for the randomized algorithm and $\tilde O(r \log \Delta + \log^3 \Delta)$ for the deterministic algorithm. #R##N#The randomized algorithm is a straightforward, though somewhat delicate, combination of an LP solver algorithm of Kuhn, Moscibroda \& Wattenhofer (2006) and randomized rounding. For the deterministic part, we extend a method of Ghaffari, Harris & Kuhn (2017) to derandomize the first-moment method; this allows us to deterministically simulate an alteration-based probabilistic construction. #R##N#This hypergraph matching algorithm has two main algorithmic consequences. First, we get nearly-optimal deterministic and randomized algorithms for the long-studied problem of maximum-weight graph matching. Specifically, we obtain a $1+\epsilon$ approximation algorithm running in $\tilde O(\log \Delta)$ randomized time and $\tilde O(\log^3 \Delta + \log^* n)$ deterministic time. These are significantly faster than prior $1+\epsilon$-approximation algorithms; furthermore, there are no constraints on the size of the edge weights. #R##N#Second, we get an algorithm for hypergraph maximal matching, which is significantly faster than the algorithm of Ghaffari, Harris & Kuhn (2017). One main consequence (along with some additional optimizations) is an algorithm which takes an arboricity-$a$ graph and generates an edge-orientation with out-degree $\lceil (1+\epsilon) a \rceil$; this runs in $\tilde O(\log^7 n \log^3 a)$ rounds deterministically or $\tilde O(\log^3 n )$ rounds randomly.

Includes members:
  Member "distributed dense subgraph detection and low outdegree orientation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "polylogarithmic time deterministic network decomposition and distributed derandomization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "distributed maximum matching verification in congest")
  Member "parameterized distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hardness of distributed optimization")
  Member "improved deterministic distributed matching via rounding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest")
  Member "deterministic distributed edge coloring with fewer colors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "deterministic distributed edge coloring via hypergraph maximal matching":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed maximum matching verification in congest")
  Member "adapting sequential algorithms to the distributed setting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hardness of distributed optimization")

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)