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: "d4m bringing associative arrays to database engines"
  Abstract: The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Numerous tools exist that allow users to store, query and index these massive quantities of data. Each storage or database engine comes with the promise of dealing with complex data. Scientists and engineers who wish to use these systems often quickly find that there is no single technology that offers a panacea to the complexity of information. When using multiple technologies, however, there is significant trouble in designing the movement of information between storage and database engines to support an end-to-end application along with a steep learning curve associated with learning the nuances of each underlying technology. In this article, we present the Dynamic Distributed Dimensional Data Model (D4M) as a potential tool to unify database and storage engine operations. Previous articles on D4M have showcased the ability of D4M to interact with the popular NoSQL Accumulo database. Recently however, D4M now operates on a variety of backend storage or database engines while providing a federated look to the end user through the use of associative arrays. In order to showcase how new databases may be supported by D4M, we describe the process of building the D4M-SciDB connector and present performance of this connection.

Connected through hyperedge HE-1 (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-2 (induced by source paper "securing hpc using federated authentication"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "enhancing hpc security with a user based firewall"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "computing on masked data a high performance method for improving big data veracity"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-3 (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"
    - "associative array model of sql nosql and newsql databases"
    - "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"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-4 (induced by source paper "interactive launch of 16 000 microsoft windows instances on a supercomputer"):
  with members:
    - "the lustre storage architecture"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "scalable system scheduling for hpc and big data"
    - "llmapreduce multi level map reduce for high performance data analysis"
    - "scheduler technologies in support of high performance data analysis"
    - "scalability of vm provisioning systems"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"

Connected through hyperedge HE-5 (induced by source paper "interactive supercomputing on 40 000 cores for machine learning and data analysis"):
  with members:
    - "benchmarking data analysis and machine learning applications on the intel knl many core processor"
    - "scalability of vm provisioning systems"
    - "tensorflow a system for large scale machine learning"
    - "enabling on demand database computing with mit supercloud database management system"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "julia a fresh approach to numerical computing"
    - "computing on masked data a high performance method for improving big data veracity"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-6 (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"
    - "polystore mathematics of relational algebra"
    - "the bigdawg polystore system and architecture"
    - "measuring user influence on twitter"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "measuring the impact of spectre and meltdown"):
  with members:
    - "the lustre storage architecture"
    - "performance measurements of supercomputing and cloud storage solutions"
    - "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"
    - "computing on masked data a high performance method for improving big data veracity"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-8 (induced by source paper "lessons learned from a decade of providing interactive on demand high performance computing to scientists and engineers"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "scalability of vm provisioning systems"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "computing on masked data a high performance method for improving big data veracity"
    - "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)