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: "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
  Abstract: The rise of big data systems has created a need for benchmarks to measure and compare the capabilities of these systems. Big data benchmarks present unique scalability challenges. The supercomputing community has wrestled with these challenges for decades and developed methodologies for creating rigorous scalable benchmarks (e.g., HPC Challenge). The proposed PageRank pipeline benchmark employs supercomputing benchmarking methodologies to create a scalable benchmark that is reflective of many real-world big data processing systems. The PageRank pipeline benchmark builds on existing prior scalable benchmarks (Graph500, Sort, and PageRank) to create a holistic benchmark with multiple integrated kernels that can be run together or independently. Each kernel is well defined mathematically and can be implemented in any programming environment. The linear algebraic nature of PageRank makes it well suited to being implemented using the GraphBLAS standard. The computations are simple enough that performance predictions can be made based on simple computing hardware models. The surrounding kernels provide the context for each kernel that allows rigorous definition of both the input and the output for each kernel. Furthermore, since the proposed PageRank pipeline benchmark is scalable in both problem size and hardware, it can be used to measure and quantitatively compare a wide range of present day and future systems. Serial implementations in C++, Python, Python with Pandas, Matlab, Octave, and Julia have been implemented and their single threaded performance has been measured.

Connected through hyperedge HE-1 (induced by source paper "sparse deep neural network graph challenge"):
  with members:
    - "radix net structured sparse matrices for deep neural networks"
    - "sparse deep neural network exact solutions"
    - "in datacenter performance analysis of a tensor processing unit"
    - "streaming graph challenge stochastic block partition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "static graph challenge subgraph isomorphism"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "enabling massive deep neural networks with the graphblas"

Connected through hyperedge HE-2 (induced by source paper "design generation and validation of extreme scale power law graphs"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "constructing adjacency arrays from incidence arrays"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"

Connected through hyperedge HE-3 (induced by source paper "graphchallenge org raising the bar on graph analytic performance"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"
    - "novel graph processor architecture prototype system and results"

Connected through hyperedge HE-4 (induced by source paper "streaming graph challenge stochastic block partition"):
  with members:
    - "static graph challenge subgraph isomorphism"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "a high performance implementation of spectral clustering on cpu gpu platforms"
    - "an incremental local first community detection method for dynamic graphs"
    - "fast detection of overlapping communities via online tensor methods"
    - "bayesian discovery of threat networks"
    - "network sampling from static to streaming graphs"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-5 (induced by source paper "static graph challenge subgraph isomorphism"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "using a power law distribution to describe big data"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "imagenet large scale visual recognition challenge"
    - "truss decomposition in massive networks"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-6 (induced by source paper "in storage embedded accelerator for sparse pattern processing"):
  with members:
    - "enforced sparse non negative matrix factorization"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "efficient subgraph matching on billion node graphs"

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)