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 "effective stiffness generalizing effective resistance sampling to finite element matrices"):
  Abstract: We define the notion of effective stiffness and show that it can used to build sparsifiers, algorithms that sparsify linear systems arising from finite-element discretizations of PDEs. In particular, we show that sampling $O(n\log n)$ elements according to probabilities derived from effective stiffnesses yields a high quality preconditioner that can be used to solve the linear system in a small number of iterations. Effective stiffness generalizes the notion of effective resistance, a key ingredient of recent progress in developing nearly linear symmetric diagonally dominant (SDD) linear solvers. Solving finite elements problems is of considerably more interest than the solution of SDD linear systems, since the finite element method is frequently used to numerically solve PDEs arising in scientific and engineering applications. Unlike SDD systems, which are relatively easy to solve, there has been limited success in designing fast solvers for finite element systems, and previous algorithms usually target discretization of limited class of PDEs like scalar elliptic or 2D trusses. Our sparsifier is general; it applies to a wide range of finite-element discretizations. A sparsifier does not constitute a complete linear solver. To construct a solver, one needs additional components (e.g., an efficient elimination or multilevel scheme for the sparsified system). Still, sparsifiers have been a critical tools in efficient SDD solvers, and we believe that our sparsifier will become a key ingredient in future fast finite-element solvers.

Includes members:
  Member "near linear work parallel sdd solvers low diameter decomposition and low stretch subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic low stretch trees via dynamic low diameter decompositions")
  Member "low rank matrix valued chernoff bounds and approximate matrix multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications")
  Member "effective resistances statistical leverage and applications to linear equation solving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved practical matrix sketching with guarantees")
  Member "faster approximate lossy generalized flow via interior point algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "graph sparsification by effective resistances":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "support graph preconditioners for 2 dimensional trusses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incomplete nested dissection")
  Member "solving elliptic finite element systems in near linear time with support preconditioners":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices")

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)