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 "faster divergence maximization for faster maximum flow"):
  Abstract: In this paper we provide an algorithm which given any $m$-edge $n$-vertex directed graph with integer capacities at most $U$ computes a maximum $s$-$t$ flow for any vertices $s$ and $t$ in $m^{4/3+o(1)}U^{1/3}$ time. This improves upon the previous best running times of $m^{11/8+o(1)}U^{1/4}$ (Liu Sidford 2019), $\tilde{O}(m \sqrt{n} \log U)$ (Lee Sidford 2014), and $O(mn)$ (Orlin 2013) when the graph is not too dense or has large capacities. #R##N#To achieve the results this paper we build upon previous algorithmic approaches to maximum flow based on interior point methods (IPMs). In particular, we overcome a key bottleneck of previous IPMs applying to maxflow (Mądry 2013, Mądry 2016, Cohen Mądry Sankowski Vladu 2017, Liu Sidford 2019, Axiotis Mądry Vladu 2020), which make progress by maximizing the energy of local $\ell_2$ norm minimizing electric flows. We generalize this approach and instead maximize the divergence of flows which minimize the Bregman divergence distance with respect to the weighted logarithmic barrier. This allows our algorithm to depend on the $\ell_\infty$ norm as opposed to the $\ell_4$ norm when bounding the amount of weight perturbation needed. Further, we show that smoothed $\ell_2$-$\ell_p$ flows (Kyng, Peng, Sachdeva, Wang 2019), which we previously used to efficiently maximize energy (Liu Sidford 2019), can also be used to efficiently maximize divergence, thereby yielding our desired runtimes. We believe both this generalized view of energy maximization and generalized flow solvers we develop may be of further interest.

Includes members:
  Member "circulation control for faster minimum cost flow in unit capacity graphs":
    also connected through other hyperedges:
      (none)
  Member "solving tall dense linear programs in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex")
  Member "solving linear programs with sqrt rank linear system solves":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "faster p norm minimizing flows via smoothed q norm problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "fast provably convergent irls algorithm for p norm linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "faster energy maximization for faster maximum flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "flows in almost linear time via adaptive preconditioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")

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)