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 arrays unified mathematics for spreadsheets databases matrices and graphs"
  Abstract: Data processing systems impose multiple views on data as it is processed by the system. These views include spreadsheets, databases, matrices, and graphs. The common theme amongst these views is the need to store and operate on data as whole sets instead of as individual data elements. This work describes a common mathematical representation of these data sets (associative arrays) that applies across a wide range of applications and technologies. Associative arrays unify and simplify these different approaches for representing and manipulating data into common two-dimensional view of data. Specifically, associative arrays (1) reduce the effort required to pass data between steps in a data processing system, (2) allow steps to be interchanged with full confidence that the results will be unchanged, and (3) make it possible to recognize when steps can be simplified or eliminated. Most database system naturally support associative arrays via their tabular interfaces. The D4M implementation of associative arrays uses this feature to provide a common interface across SQL, NoSQL, and NewSQL databases.

Connected through hyperedge HE-1 (induced by source paper "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"):
  with members:
    - "pipegen data pipe generator for hybrid analytics"
    - "using a power law distribution to describe big data"
    - "graphs matrices and the graphblas seven good reasons"
    - "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"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-2 (induced by source paper "benchmarking the graphulo processing framework"):
  with members:
    - "using a power law distribution to describe big data"
    - "graphs matrices and the graphblas seven good reasons"
    - "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"
    - "big data dimensional analysis"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-3 (induced by source paper "modelwizard toward interactive model construction"):
  with members:
    - "the sql query language configurable unifying and semi structured"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "probabilistic programming concepts"
    - "compiling relational database schemata into probabilistic graphical models"
    - "operations for learning with graphical models"

Connected through hyperedge HE-4 (induced by source paper "d4m bringing associative arrays to database engines"):
  with members:
    - "graphulo linear algebra graph kernels for nosql databases"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-5 (induced by source paper "parallel vectorized algebraic aes in matlab for rapid prototyping of encrypted sensor processing algorithms and database analytics"):
  with members:
    - "computing on masked data a high performance method for improving big data veracity"

Connected through hyperedge HE-6 (induced by source paper "big data strategies for data center infrastructure management using a 3d gaming platform"):
  with members:
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

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)