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 "on two measures of distance between fully labelled trees"):
  Abstract: The last decade brought a significant increase in the amount of data and a variety of new inference methods for reconstructing the detailed evolutionary history of various cancers. This brings the need of designing efficient procedures for comparing rooted trees representing the evolution of mutations in tumor phylogenies. Bernardini et al. [CPM 2019] recently introduced a notion of the rearrangement distance for fully-labelled trees motivated by this necessity. This notion originates from two operations: one that permutes the labels of the nodes, the other that affects the topology of the tree. Each operation alone defines a distance that can be computed in polynomial time, while the actual rearrangement distance, that combines the two, was proven to be NP-hard. #R##N#We answer two open question left unanswered by the previous work. First, what is the complexity of computing the permutation distance? Second, is there a constant-factor approximation algorithm for estimating the rearrangement distance between two arbitrary trees? We answer the first one by showing, via a two-way reduction, that calculating the permutation distance between two trees on $n$ nodes is equivalent, up to polylogarithmic factors, to finding the largest cardinality matching in a sparse bipartite graph. In particular, by plugging in the algorithm of Mądry [FOCS 2013], we obtain an $O(n^{10/7})$ time algorithm for computing the permutation distance between two trees on $n$ nodes. Then we answer the second question positively, and design a linear-time constant-factor approximation algorithm that does not need any assumption on the trees.

Includes members:
  Member "a rearrangement distance for fully labelled trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "funseqset towards a purely functional data structure for the linearisation case of dynamic trees problem")
  Member "new algorithms and lower bounds for all pairs max flow in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "computing quartet distance is equivalent to counting 4 cycles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalences between triangle and range query problems")
  Member "conditional lower bounds for all pairs max flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new algorithms and lower bounds for all pairs max flow in undirected graphs")
  Member "a faster construction of greedy consensus trees":
    also connected through other hyperedges:
      (none)
  Member "inferring clonal evolution of tumors from single nucleotide somatic mutations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a rearrangement distance for fully labelled trees")
  Member "a decomposition theorem for maximum weight bipartite matchings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "experimental evaluation of modified decomposition algorithm for maximum weight bipartite matching")

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)