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: "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
  Abstract: Google BigTable's scale-out design for distributed key-value storage inspired a generation of NoSQL databases. Recently the NewSQL paradigm emerged in response to analytic workloads that demand distributed computation local to data storage. Many such analytics take the form of graph algorithms, a trend that motivated the GraphBLAS initiative to standardize a set of matrix math kernels for building graph algorithms. In this article we show how it is possible to implement the GraphBLAS kernels in a BigTable database by presenting the design of Graphulo, a library for executing graph algorithms inside the Apache Accumulo database. We detail the Graphulo implementation of two graph algorithms and conduct experiments comparing their performance to two main-memory matrix math systems. Our results shed insight into the conditions that determine when executing a graph algorithm is faster inside a database versus an external system—in short, that memory requirements and relative I/O are critical factors.

Connected through hyperedge HE-1 (induced by source paper "a graphblas approach for subgraph counting"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "high performance massive subgraph counting using pipelined adaptive group communication"
    - "accelerating pagerank using partition centric processing"
    - "subgraph counting color coding beyond trees"
    - "the reverse cuthill mckee algorithm in distributed memory"
    - "mathematical foundations of the graphblas"
    - "ringo interactive graph analytics on big memory machines"
    - "graphs matrices and the graphblas seven good reasons"

Connected through hyperedge HE-2 (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"
    - "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"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-3 (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"
    - "using a power law distribution to describe big data"

Connected through hyperedge HE-4 (induced by source paper "laradb a minimalist kernel for linear and relational algebra computation"):
  with members:
    - "lara a key value algebra underlying arrays and relations"
    - "associative array model of sql nosql and newsql databases"
    - "mathematical foundations of the graphblas"
    - "using a power law distribution to describe big data"
    - "mllib machine learning in apache spark"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "big data dimensional analysis"

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.DB(Databases)