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 deterministic distributed matching via rounding"):
  Abstract: We present improved deterministic distributed algorithms for a number of well-studied matching problems, which are simpler, faster, more accurate, and/or more general than their known counterparts. The common denominator of these results is a deterministic distributed rounding method for certain linear programs, which is the first such rounding method, to our knowledge. A sampling of our end results is as follows. #R##N#-- An $O(\log^2 \Delta\cdot \log n)$-round deterministic distributed algorithm for computing a maximal matching, in $n$-node graphs with maximum degree $\Delta$. This is the first improvement in about 20 years over the celebrated $O(\log^4 n)$-round algorithm of Ha\'n\'ckowiak, Karo\'nski, and Panconesi [SODA'98, PODC'99]. #R##N#-- A deterministic distributed algorithm for computing a $(2+\varepsilon)$-approximation of maximum matching in $O(\log^2 \Delta \cdot \log \frac{1}{\varepsilon} + \log^ * n)$ rounds. This is exponentially faster than the classic $O(\Delta +\log^* n)$-round $2$-approximation of Panconesi and Rizzi [DIST'01]. With some modifications, the algorithm can also find an $\varepsilon$-maximal matching which leaves only an $\varepsilon$-fraction of the edges on unmatched nodes. #R##N#-- An $O(\log^2 \Delta \cdot \log \frac{1}{\varepsilon} + \log^ * n)$-round deterministic distributed algorithm for computing a $(2+\varepsilon)$-approximation of a maximum weighted matching, and also for the more general problem of maximum weighted $b$-matching. These improve over the $O(\log^4 n \cdot \log_{1+\varepsilon} W)$-round $(6+\varepsilon)$-approximation algorithm of Panconesi and Sozio [DIST'10], where $W$ denotes the maximum normalized weight.

Includes members:
  Member "distributed degree splitting edge coloring and orientations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "an exponential separation between randomized and deterministic complexity in the local model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants")
  Member "on the complexity of local distributed graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "an improved distributed algorithm for maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples")
  Member "linear in delta lower bounds in the local model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lower bounds for maximal matchings and maximal independent sets")
  Member "toward more localized local algorithms removing assumptions concerning global knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster deterministic distributed coloring through recursive list coloring")
  Member "distributed maximal matching greedy is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants")
  Member "the locality of distributed symmetry breaking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed maximum matching verification in congest")

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)