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: "polystore mathematics of relational algebra"
  Abstract: Financial transactions, internet search, and data analysis are all placing increasing demands on databases. SQL, NoSQL, and NewSQL databases have been developed to meet these demands and each offers unique benefits. SQL, NoSQL, and NewSQL databases also rely on different underlying mathematical models. Polystores seek to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Integrating the underlying mathematics of these diverse databases can be an important enabler for polystores as it enables effective reasoning across different databases. Associative arrays provide a common approach for the mathematics of polystores by encompassing the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). Prior work presented the SQL relational model in terms of associative arrays and identified key mathematical properties that are preserved within SQL. This work provides the rigorous mathematical definitions, lemmas, and theorems underlying these properties. Specifically, SQL Relational Algebra deals primarily with relations - multisets of tuples - and operations on and between these relations. These relations can be modeled as associative arrays by treating tuples as non-zero rows in an array. Operations in relational algebra are built as compositions of standard operations on associative arrays which mirror their matrix counterparts. These constructions provide insight into how relational algebra can be handled via array operations. As an example application, the composition of two projection operations is shown to also be a projection, and the projection of a union is shown to be equal to the union of the projections.

Connected through hyperedge HE-1 (induced by source paper "ai data wrangling with associative arrays"):
  with members:
    - "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"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-2 (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"
    - "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"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

Connected through hyperedge HE-3 (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"
    - "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"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

Connected through hyperedge HE-4 (induced by source paper "on matrices and k relations"):
  with members:
    - "on the expressive power of linear algebra on graphs"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "on the expressive power of query languages for matrices"
    - "juggling functions inside a database"
    - "faq questions asked frequently"

Connected through hyperedge HE-5 (induced by source paper "sparse deep neural network exact solutions"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "enabling massive deep neural networks with the graphblas"
    - "theory ii landscape of the empirical risk in deep learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fast algorithms for convolutional neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

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"
    - "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"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-7 (induced by source paper "a tensor based data model for polystore an application to social networks data"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "the bigdawg polystore system and architecture"
    - "measuring user influence on twitter"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "the sql query language configurable unifying and semi structured"
    - "predicting successful memes using network and community structure"
    - "exposing multi relational networks to single relational network analysis algorithms"

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)