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: "associative array model of sql nosql and newsql databases"
  Abstract: The success of SQL, NoSQL, and NewSQL databases is a reflection of their ability to provide significant functionality and performance benefits for specific domains, such as financial transactions, internet search, and data analysis. The BigDAWG polystore seeks to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Associative arrays provide a common approach to the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). This work presents the SQL relational model in terms of associative arrays and identifies the key mathematical properties that are preserved within SQL. These properties include associativity, commutativity, distributivity, identities, annihilators, and inverses. Performance measurements on distributivity and associativity show the impact these properties can have on associative array operations. These results demonstrate that associative arrays could provide a mathematical model for polystores to optimize the exchange of data and execution queries.

Connected through hyperedge HE-1 (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"
    - "the bigdawg polystore system and architecture"
    - "mathematical foundations of the graphblas"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-2 (induced by source paper "ai enabling technologies a survey"):
  with members:
    - "the lustre storage architecture"
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "this looks like that deep learning for interpretable image recognition"
    - "gazelle a low latency framework for secure neural network inference"
    - "tracking emerges by colorizing videos"
    - "robust physical world attacks on deep learning models"

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"
    - "benchmarking scidb data import on hpc systems"
    - "lustre hadoop accumulo"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

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"
    - "benchmarking scidb data import on hpc systems"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

Connected through hyperedge HE-5 (induced by source paper "hyperscaling internet graph analysis with d4m on the mit supercloud"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "llmapreduce multi level map reduce for high performance data analysis"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "tensorflow a system for large scale machine learning"
    - "using a power law distribution to describe big data"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-6 (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"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-7 (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"
    - "the bigdawg polystore system and architecture"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

Connected through hyperedge HE-8 (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"
    - "mathematical foundations of the graphblas"
    - "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"
    - "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)