human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "block locally optimal preconditioned eigenvalue xolvers blopex in hypre and petsc"
  Abstract: We describe our software package Block Locally Optimal Preconditioned Eigenvalue Xolvers (BLOPEX) recently publicly released. BLOPEX is available as a stand-alone serial library, as an external package to PETSc (Portable, Extensible Toolkit for Scientific Computation, a general purpose suite of tools developed by Argonne National Laboratory for the scalable solution of partial differential equations and related problems), and is also built into hypre (High Performance Preconditioners, a scalable linear solvers package developed by Lawrence Livermore National Laboratory). The present BLOPEX release includes only one solver—the Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) method for symmetric eigenvalue problems. hypre provides users with advanced high-quality parallel multigrid preconditioners for linear systems. With BLOPEX, the same preconditioners can now be efficiently used for symmetric eigenvalue problems. PETSc facilitates the integration of independently developed application modules, with strict attention to component interoperability, and makes BLOPEX extremely easy to compile and use with preconditioners that are available via PETSc. We present the LOBPCG algorithm in BLOPEX for hypre and PETSc. We demonstrate numerically the scalability of BLOPEX by testing it on a number of distributed and shared memory parallel systems, including a Beowulf system, SUN Fire 880, an AMD dual-core Opteron workstation, and IBM BlueGene/L supercomputer, using PETSc domain decomposition and hypre multigrid preconditioning. We test BLOPEX on a model problem, the standard 7-point finite-difference approximation of the 3-D Laplacian, with the problem size in the range of $10^5$-$10^8$.

Connected through hyperedge HE-1 (induced by source paper "on spectral partitioning of signed graphs"):
  with members:
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "megaman manifold learning with millions of points"
    - "effective deterministic initialization for k means like methods via local density peaks searching"
    - "first principles multiway spectral partitioning of graphs"
    - "the hidden convexity of spectral clustering"
    - "the slashdot zoo mining a social network with negative edges"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "signal reconstruction via operator guiding"
    - "megaman manifold learning with millions of points"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "recent implementations applications and extensions of the locally optimal block preconditioned conjugate gradient method lobpcg"):
  with members:
    - "accelerated graph based spectral polynomial filters"

Connected through hyperedge HE-4 (induced by source paper "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "a high performance parallel algorithm for nonnegative matrix factorization"
    - "mllib machine learning in apache spark"
    - "matrix computations and optimization in apache spark"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "randomized algorithms for matrices and data"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-5 (induced by source paper "an optimized and scalable eigensolver for sequences of eigenvalue problems"):
  with members:
    - "block iterative eigensolvers for sequences of correlated eigenvalue problems"
    - "a parallel and scalable iterative solver for sequences of dense eigenproblems arising in flapw"
    - "solving dense generalized eigenproblems on multi threaded architectures"
    - "high performance solvers for dense hermitian eigenproblems"

Connected through hyperedge HE-6 (induced by source paper "communication optimal parallel and sequential qr and lu factorizations theory and practice"):
  with members:
    - "a class of parallel tiled linear algebra algorithms for multicore architectures"

Question: Please tell me which class the center node 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.MS(Mathematical Software)