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: "benchmarking scidb data import on hpc systems"
  Abstract: SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting data for analysis. It is designed to be massively parallel and can run on commodity hardware in a high performance computing (HPC) environment. In this paper, we present the performance of SciDB using simulated image data. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a cluster running the MIT SuperCloud software stack. A peak performance of 2.2M database inserts per second was achieved on a single node of this system. We also show that SciDB and the D4M toolbox provide more efficient ways to access random sub-volumes of massive datasets compared to the traditional approaches of reading volumetric data from individual files. This work describes the D4M and SciDB tools we developed and presents the initial performance results. This performance was achieved by using parallel inserts, a in-database merging of arrays as well as supercomputing techniques, such as distributed arrays and single-program-multiple-data programming.

Connected through hyperedge HE-1 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices"):
  with members:
    - "streaming 1 9 billion hypersparse network updates per second with d4m"
    - "hypersparse neural network analysis of large scale internet traffic"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "streaming graph challenge stochastic block partition"
    - "static graph challenge subgraph isomorphism"
    - "mathematical foundations of the graphblas"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

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"
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "associative array model of sql nosql and newsql databases"
    - "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"
    - "polystore mathematics of relational algebra"
    - "associative array model of sql nosql and newsql databases"
    - "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 "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"
    - "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"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-5 (induced by source paper "mit supercloud portal workspace enabling hpc web application deployment"):
  with members:
    - "enhancing hpc security with a user based firewall"
    - "enabling on demand database computing with mit supercloud database management system"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

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)