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 "nearly maximum flows in nearly linear time"):
  Abstract: We introduce a new approach to the maximum flow problem in undirected, capacitated graphs using $\alpha$-\emph{congestion-approximators}: easy-to-compute functions that approximate the congestion required to route single-commodity demands in a graph to within a factor of $\alpha$. Our algorithm maintains an arbitrary flow that may have some residual excess and deficits, while taking steps to minimize a potential function measuring the congestion of the current flow plus an over-estimate of the congestion required to route the residual demand. Since the residual term over-estimates, the descent process gradually moves the contribution to our potential function from the residual term to the congestion term, eventually achieving a flow routing the desired demands with nearly minimal congestion after $\tilde{O}(\alpha\eps^{-2}\log^2 n)$ iterations. Our approach is similar in spirit to that used by Spielman and Teng (STOC 2004) for solving Laplacian systems, and we summarize our approach as trying to do for $\ell_\infty$-flows what they do for $\ell_2$-flows. #R##N#Together with a nearly linear time construction of a $n^{o(1)}$-congestion-approximator, we obtain $1+\eps$-optimal single-commodity flows undirected graphs in time $m^{1+o(1)}\eps^{-2}$, yielding the fastest known algorithm for that problem. Our requirements of a congestion-approximator are quite low, suggesting even faster and simpler algorithms for certain classes of graphs. For example, an $\alpha$-competitive oblivious routing tree meets our definition, \emph{even without knowing how to route the tree back in the graph}. For graphs of conductance $\phi$, a trivial $\phi^{-1}$-congestion-approximator gives an extremely simple algorithm for finding $1+\eps$-optimal-flows in time $\tilde{O}(m\phi^{-1})$.

Includes members:
  Member "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "a simple combinatorial algorithm for solving sdd systems in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "fast approximation algorithms for cut based problems in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spectral approach to network design")
  Member "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "randomized approximation schemes for cuts and flows in capacitated graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and simple algorithms for fault tolerant spanners")

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)