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 "reoptimization via gradual transformations"):
  Abstract: This paper introduces a natural "reoptimization" meta-problem, which should be particularly relevant in faulty or dynamic networks. Fix any $\Delta > 0, \epsilon > 0$. Given two "approximate" solutions $M$ and $M'$ to some graph optimization problem, where $M'$ is "better" than $M$, the goal is to gradually transform $M$ into $M'$ throughout a sequence of "phases", each making at most $\Delta$ changes to the current (gradually transformed) solution, so that the solution at the end of each phase is feasible and at least as good, up to some $\epsilon$ dependence, as the original solution $M$. We study (approximate) maximum cardinality matching, maximum weight matching, and minimum spanning forest, and design near-optimal transformations for these problems. #R##N#We demonstrate the applicability of this meta-problem to dynamic graph matchings. The number of changes to the maintained matching per update step, known as the recourse bound, is an important measure of quality. Nevertheless, the worst-case recourse bounds of almost all known dynamic matching algorithms is significantly larger than the corresponding update times. We fill in this gap via a surprisingly simple black-box reduction: Any dynamic algorithm for maintaining a $\beta$-approximate maximum cardinality matching with update time $T$, for any $\beta \ge 1, T, \epsilon > 0$, can be transformed into an algorithm for maintaining a $(\beta(1 +\epsilon))$-approximate maximum cardinality matching with update time $T + O(1/\epsilon)$ and a worst-case recourse bound of $O(1/\epsilon)$. This result generalizes for approximate maximum weight matching. As a corollary of our reduction, several key dynamic approximate matching algorithms in this area, which achieve low update time bounds but poor worst-case recourse bounds, are strengthened to achieve near-optimal worst-case recourse bounds with essentially no loss in the update time.

Includes members:
  Member "fully dynamic maximal independent set with sublinear update time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "new algorithms for steiner tree reoptimization":
    also connected through other hyperedges:
      (none)
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "fully dynamic almost maximal matching breaking the polynomial barrier for worst case time bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rounding dynamic matchings against an adaptive adversary")
  Member "the shortest augmenting paths for online matchings on trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved dynamic graph coloring")
  Member "a tight bound for shortest augmenting paths on trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online graph matching problems with a worst case reassignment budget")
  Member "fully dynamic maximal matching in constant update time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")

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)