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 linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"):
  Abstract: We present a randomized algorithm that, on input a symmetric, weakly diagonally dominant n-by-n matrix A with m nonzero entries and an n-vector b, produces a y such that $\norm{y - \pinv{A} b}_{A} \leq \epsilon \norm{\pinv{A} b}_{A}$ in expected time $O (m \log^{c}n \log (1/\epsilon)),$ for some constant c. By applying this algorithm inside the inverse power method, we compute approximate Fiedler vectors in a similar amount of time. The algorithm applies subgraph preconditioners in a recursive fashion. These preconditioners improve upon the subgraph preconditioners first introduced by Vaidya (1990). #R##N#For any symmetric, weakly diagonally-dominant matrix A with non-positive off-diagonal entries and $k \geq 1$, we construct in time $O (m \log^{c} n)$ a preconditioner B of A with at most $2 (n - 1) + O ((m/k) \log^{39} n)$ nonzero off-diagonal entries such that the finite generalized condition number $\kappa_{f} (A,B)$ is at most k, for some other constant c. #R##N#In the special case when the nonzero structure of the matrix is planar the corresponding linear system solver runs in expected time $ O (n \log^{2} n + n \log n \ \log \log n \ \log (1/\epsilon))$. #R##N#We hope that our introduction of algorithms of low asymptotic complexity will lead to the development of algorithms that are also fast in practice.

Includes members:
  Member "a simple combinatorial algorithm for solving sdd systems in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "faster approximate multicommodity flow using quadratically coupled flows":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "a nearly mlogn time solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral sparsification via bounded independence sampling")
  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 "a note on preconditioning by low stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic low stretch trees via dynamic low diameter decompositions")
  Member "faster generation of random spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "subgraph sparsification and nearly optimal ultrasparsifiers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a spectral approach to network design")
  Member "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressive sensing for cut improvement and local clustering")

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.NA(Numerical Analysis)