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: "performance engineering of the kernel polynomal method on large scale cpu gpu systems"
  Abstract: The Kernel Polynomial Method (KPM) is a well-established scheme in quantum physics and quantum chemistry to determine the Eigen value density and spectral properties of large sparse matrices. In this work we demonstrate the high optimization potential and feasibility of peta-scale heterogeneous CPU-GPU implementations of the KPM. At the node level we show that it is possible to decouple the sparse matrix problem posed by KPM from main memory bandwidth both on CPU and GPU. To alleviate the effects of scattered data access we combine loosely coupled outer iterations with tightly coupled block sparse matrix multiple vector operations, which enables pure data streaming. All optimizations are guided by a performance analysis and modelling process that indicates how the computational bottlenecks change with each optimization step. Finally we use the optimized node-level KPM with a hybrid-parallel framework to perform large-scale heterogeneous electronic structure calculations for novel topological materials on a pet scale-class Cray XC30 system.

Connected through hyperedge HE-1 (induced by source paper "desynchronization and wave pattern formation in mpi parallel and hybrid memory bound programs"):
  with members:
    - "bridging the architecture gap abstracting performance relevant properties of modern server processors"
    - "propagation and decay of injected one off delays on clusters a case study"
    - "chebyshev filter diagonalization on modern manycore processors and gpgpus"
    - "quantifying performance bottlenecks of stencil computations using the execution cache memory model"

Connected through hyperedge HE-2 (induced by source paper "chebyshev filter diagonalization on modern manycore processors and gpgpus"):
  with members:
    - "ghost building blocks for high performance sparse linear algebra on heterogeneous systems"
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"
    - "efficient estimation of eigenvalue counts in an interval"
    - "sparse matrix vector multiplication on gpgpu clusters a new storage format and a scalable implementation"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"

Connected through hyperedge HE-3 (induced by source paper "ghost building blocks for high performance sparse linear algebra on heterogeneous systems"):
  with members:
    - "pipelined iterative solvers with kernel fusion for graphics processing units"
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"
    - "asynchronous mpi for the masses"
    - "density matrix based algorithm for solving eigenvalue problems"
    - "pt scotch a tool for efficient parallel graph ordering"

Connected through hyperedge HE-4 (induced by source paper "field programmable crossbar array fpca for reconfigurable computing"):
  with members:
    - "a framework for general sparse matrix matrix multiplication on gpus and heterogeneous processors"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"

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.CE(Computational Engineering, Finance, and Science)