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: "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
  Abstract: Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. The scaling of existing parallel implementations of SpGEMM is heavily bound by communication. Even though 3D (or 2.5D) algorithms have been proposed and theoretically analyzed in the flat MPI model on Erdos--Renyi matrices, those algorithms had not been implemented in practice and their complexities had not been analyzed for the general case. In this work, we present the first implementation of the 3D SpGEMM formulation that exploits multiple (intranode and internode) levels of parallelism, achieving significant speedups over the state-of-the-art publicly available codes at all levels of concurrencies. We extensively evaluate our implementation and identify bottlenecks that should be subject to further research.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "high performance sparse matrix matrix products on intel knl and multicore architectures"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "sparch efficient architecture for sparse matrix multiplication"):
  with members:
    - "practical sparse matrices in c with hybrid storage and template based expression optimisation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning to design circuits"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "the parallelism motifs of genomic data analysis"):
  with members:
    - "dibella distributed long read to long read alignment"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "extreme scale de novo metagenome assembly"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-4 (induced by source paper "optimizing high performance markov clustering for pre exascale architectures"):
  with members:
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-5 (induced by source paper "bandwidth optimized parallel algorithms for sparse matrix matrix multiplication using propagation blocking"):
  with members:
    - "fast randomized pca for sparse data"
    - "a high performance implementation of spectral clustering on cpu gpu platforms"

Connected through hyperedge HE-6 (induced by source paper "communication efficient jaccard similarity for high performance distributed genome comparisons"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "a note on the triangle inequality for the jaccard distance"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "mathematical foundations of the graphblas"
    - "sparse tensor algebra as a parallel programming model"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"
    - "minimizing communication in linear algebra"

Connected through hyperedge HE-7 (induced by source paper "parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model"):
  with members:
    - "ductteip an efficient programming model for distributed task based parallel computing"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
    - "chunks and tasks a programming model for parallelization of dynamic algorithms"
    - "parallel sparse matrix matrix multiplication and indexing implementation and experiments"

Connected through hyperedge HE-8 (induced by source paper "sparse matrix multiplication with bandwidth restricted all to all communication"):
  with members:
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "mst in o 1 rounds of the congested clique"
    - "increasing the efficiency of sparse matrix matrix multiplication with a 2 5d algorithm and one sided mpi"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "towards a complexity theory for the congested clique"
    - "tight bounds for distributed graph computations"
    - "further algebraic algorithms in the congested clique model and applications to graph theoretic problems"
    - "hypergraph partitioning for sparse matrix matrix multiplication"

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)