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: "a performance comparison of cuda and opencl"
  Abstract: CUDA and OpenCL are two different frameworks for GPU programming. OpenCL is an open standard that can be used to program CPUs, GPUs, and other devices from different vendors, while CUDA is specific to NVIDIA GPUs. Although OpenCL promises a portable language for GPU programming, its generality may entail a performance penalty. In this paper, we use complex, near-identical kernels from a Quantum Monte Carlo application to compare the performance of CUDA and OpenCL. We show that when using NVIDIA compiler tools, converting a CUDA kernel to an OpenCL kernel involves minimal modifications. Making such a kernel compile with ATI's build tools involves more modifications. Our performance tests measure and compare data transfer times to and from the GPU, kernel execution times, and end-to-end application execution times for both CUDA and OpenCL.

Connected through hyperedge HE-1 (induced by source paper "a unified optimization approach for cnn model inference on integrated gpus"):
  with members:
    - "neurostream scalable and energy efficient deep learning with smart memory cubes"
    - "inference of quantized neural networks on heterogeneous all programmable devices"
    - "yolov3 an incremental improvement"
    - "learning to optimize tensor programs"
    - "optimizing cnn model inference on cpus"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "embedded binarized neural networks"

Connected through hyperedge HE-2 (induced by source paper "gpu computing with python performance energy efficiency and usability"):
  with members:
    - "benchmarking opencl openacc openmp and cuda programming productivity performance and energy consumption"
    - "a comparative study of programming languages in rosetta code"
    - "best practices for scientific computing"
    - "pycuda and pyopencl a scripting based approach to gpu run time code generation"

Connected through hyperedge HE-3 (induced by source paper "pipelined iterative solvers with kernel fusion for graphics processing units"):
  with members:
    - "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units"
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "biomolecular electrostatics using a fast multipole bem on up to 512 gpus and a billion unknowns"

Connected through hyperedge HE-4 (induced by source paper "group communication patterns for high performance computing in scala"):
  with members:
    - "foopar a functional object oriented parallel framework in scala"
    - "building blocks for performance oriented dsls"

Connected through hyperedge HE-5 (induced by source paper "nlsemagic nonlinear schrodinger equation multi dimensional matlab based gpu accelerated integrators using compact high order schemes"):
  with members:
    - "a modulus squared dirichlet boundary condition for time dependent complex partial differential equations and its application to the nonlinear schr odinger equation"
    - "numerical stability of explicit runge kutta finite difference schemes for the nonlinear schr odinger equation"
    - "a two step high order compact scheme for the laplacian operator and its implementation in an explicit method for integrating the nonlinear schr odinger equation"

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.PF(Performance)