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: "high performance sparse matrix matrix products on intel knl and multicore architectures"
  Abstract: Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is widely used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. Although many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We examine their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. A critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "static graph challenge subgraph isomorphism"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
    - "a survey of sparse matrix vector multiplication performance on large matrices"

Connected through hyperedge HE-2 (induced by source paper "fast sparse convnets"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "design principles for sparse matrix multiplication on the gpu"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"

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.DC(Distributed, Parallel, and Cluster Computing)