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 "local conflict coloring"):
  Abstract: Locally finding a solution to symmetry-breaking tasks such as vertex-coloring, edge-coloring, maximal matching, maximal independent set, etc., is a long-standing challenge in distributed network computing. More recently, it has also become a challenge in the framework of centralized local computation. We introduce conflict coloring as a general symmetry-breaking task that includes all the aforementioned tasks as specific instantiations --- conflict coloring includes all locally checkable labeling tasks from [Naor\&Stockmeyer, STOC 1993]. Conflict coloring is characterized by two parameters $l$ and $d$, where the former measures the amount of freedom given to the nodes for selecting their colors, and the latter measures the number of constraints which colors of adjacent nodes are subject to.We show that, in the standard LOCAL model for distributed network computing, if $l/d \textgreater{} \Delta$, then conflict coloring can be solved in $\tilde O(\sqrt{\Delta})+\log^*n$ rounds in $n$-node graphs with maximum degree $\Delta$, where $\tilde O$ ignores the polylog factors in $\Delta$. The dependency in~$n$ is optimal, as a consequence of the $\Omega(\log^*n)$ lower bound by [Linial, SIAM J. Comp. 1992] for $(\Delta+1)$-coloring. An important special case of our result is a significant improvement over the best known algorithm for distributed $(\Delta+1)$-coloring due to [Barenboim, PODC 2015], which required $\tilde O(\Delta^{3/4})+\log^*n$ rounds. Improvements for other variants of coloring, including $(\Delta+1)$-list-coloring, $(2\Delta-1)$-edge-coloring, $T$-coloring, etc., also follow from our general result on conflict coloring. Likewise, in the framework of centralized local computation algorithms (LCAs), our general result yields an LCA which requires a smaller number of probes than the previously best known algorithm for vertex-coloring, and works for a wide range of coloring problems.

Includes members:
  Member "an exponential separation between randomized and deterministic complexity in the local model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "truly tight in delta bounds for bipartite maximal matching and variants")
  Member "linear in delta lower bounds in the local model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "faster deterministic distributed coloring through recursive list coloring")
  Member "a local computation approximation scheme to maximum matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "distributed maximal matching greedy is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "distributed maximum matching verification in congest")
  Member "converting online algorithms to local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")
  Member "fast local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local access to huge random objects through partial sampling")

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)