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 work efficient parallel sparse matrix sparse vector multiplication algorithm"
  Abstract: We design and develop a work-efficient multithreaded algorithm for sparse matrix-sparse vector multiplication (SpMSpV) where the matrix, the input vector, and the output vector are all sparse. SpMSpV is an important primitive in the emerging GraphBLAS standard and is the workhorse of many graph algorithms including breadth-first search, bipartite graph matching, and maximal independent set. As thread counts increase, existing multithreaded SpMSpV algorithms can spend more time accessing the sparse matrix data structure than doing arithmetic. Our shared-memory parallel SpMSpV algorithm is work efficient in the sense its total work is proportional to the number of arithmetic operations required. The key insight is to avoid each thread individually scan the list of matrix columns. #R##N#Our algorithm is simple to implement and operates on existing column-based sparse matrix formats. It performs well on diverse matrices and vectors with heterogeneous sparsity patterns. A high-performance implementation of the algorithm attains up to 15x speedup on a 24-core Intel Ivy Bridge processor and up to 49x speedup on a 64-core Intel KNL manycore processor. In contrast to implementations of existing algorithms, the performance of our algorithm is sustained on a variety of different input types include matrices representing scale-free and high-diameter graphs.

Connected through hyperedge HE-1 (induced by source paper "fastsv a distributed memory connected component algorithm with fast convergence"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "composing optimization techniques for vertex centric graph processing via communication channels"
    - "an adaptive parallel algorithm for computing connected components"
    - "finding connected components on map reduce in logarithmic rounds"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-2 (induced by source paper "optimal algebraic breadth first search for sparse graphs"):
  with members:
    - "implementing push pull efficiently in graphblas"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-3 (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions"):
  with members:
    - "partitionedvc partitioned external memory graph analytics framework for ssds"
    - "graphit a high performance dsl for graph analytics"
    - "accelerating pagerank using partition centric processing"
    - "parallel local graph clustering"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-4 (induced by source paper "implementing push pull efficiently in graphblas"):
  with members:
    - "gunrock gpu graph analytics"
    - "the power of sparsity in convolutional neural networks"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "parallel breadth first search on distributed memory systems"

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)