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: "graphulo linear algebra graph kernels for nosql databases"
  Abstract: Big data and the Internet of Things era continue to challenge computational systems. Several technology solutions such as NoSQL databases have been developed to deal with this challenge. In order to generate meaningful results from large datasets, analysts often use a graph representation which provides an intuitive way to work with the data. Graph vertices can represent users and events, and edges can represent the relationship between vertices. Graph algorithms are used to extract meaningful information from these very large graphs. At MIT, the Graphulo initiative is an effort to perform graph algorithms directly in NoSQL databases such as Apache Accumulo or SciDB, which have an inherently sparse data storage scheme. Sparse matrix operations have a history of efficient implementations and the Graph Basic Linear Algebra Subprogram (Graph BLAS) community has developed a set of key kernels that can be used to develop efficient linear algebra operations. However, in order to use the Graph BLAS kernels, it is important that common graph algorithms be recast using the linear algebra building blocks. In this article, we look at common classes of graph algorithms and recast them into linear algebra operations using the Graph BLAS building blocks.

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 "ai data wrangling with associative arrays"):
  with members:
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "associative array model of sql nosql and newsql databases"
    - "the bigdawg polystore system and architecture"
    - "mathematical foundations of the graphblas"

Connected through hyperedge HE-3 (induced by source paper "streaming 1 9 billion hypersparse network updates per second with d4m"):
  with members:
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "streaming graph challenge stochastic block partition"
    - "static graph challenge subgraph isomorphism"
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "associative array model of sql nosql and newsql databases"
    - "benchmarking scidb data import on hpc systems"
    - "lustre hadoop accumulo"

Connected through hyperedge HE-4 (induced by source paper "a billion updates per second using 30 000 hierarchical in memory d4m databases"):
  with members:
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "polystore mathematics of relational algebra"
    - "associative array model of sql nosql and newsql databases"
    - "benchmarking scidb data import on hpc systems"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

Connected through hyperedge HE-5 (induced by source paper "tabularosa tabular operating system architecture for massively parallel heterogeneous compute engines"):
  with members:
    - "scalable system scheduling for hpc and big data"
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "novel graph processor architecture prototype system and results"
    - "associative array model of sql nosql and newsql databases"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-6 (induced by source paper "d4m 3 0 extended database and language capabilities"):
  with members:
    - "benchmarking the graphulo processing framework"
    - "the bigdawg polystore system and architecture"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "benchmarking scidb data import on hpc systems"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "using a power law distribution to describe big data"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "enabling on demand database computing with mit supercloud database management system"

Connected through hyperedge HE-7 (induced by source paper "distributed triangle counting in the graphulo matrix math library"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "static graph challenge subgraph isomorphism"
    - "benchmarking the graphulo processing framework"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"

Connected through hyperedge HE-8 (induced by source paper "polystore mathematics of relational algebra"):
  with members:
    - "version 0 1 of the bigdawg polystore system"
    - "constructing adjacency arrays from incidence arrays"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "d4m 3 0 extended database and language capabilities"
    - "bigdawg polystore release and demonstration"
    - "associative array model of sql nosql and newsql databases"
    - "the bigdawg polystore system and architecture"
    - "julia implementation of the dynamic distributed dimensional data model"

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.DS(Data Structures and Algorithms)