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: "big data dimensional analysis"
  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. One of the main challenges associated with big data variety is automatically understanding the underlying structures and patterns of the data. Such an understanding is required as a pre-requisite to the application of advanced analytics to the data. Further, big data sets often contain anomalies and errors that are difficult to know a priori. Current approaches to understanding data structure are drawn from the traditional database ontology design. These approaches are effective, but often require too much human involvement to be effective for the volume, velocity and variety of data encountered by big data systems. Dimensional Data Analysis (DDA) is a proposed technique that allows big data analysts to quickly understand the overall structure of a big dataset, determine anomalies. DDA exploits structures that exist in a wide class of data to quickly determine the nature of the data and its statical anomalies. DDA leverages existing schemas that are employed in big data databases today. This paper presents DDA, applies it to a number of data sets, and measures its performance. The overhead of DDA is low and can be applied to existing big data systems without greatly impacting their computing requirements.

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 "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"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

Connected through hyperedge HE-3 (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"
    - "associative array model of sql nosql and newsql databases"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "benchmarking the graphulo processing framework"):
  with members:
    - "using a power law distribution to describe big data"
    - "associative arrays unified mathematics for spreadsheets databases matrices and graphs"
    - "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"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-5 (induced by source paper "julia implementation of the dynamic distributed dimensional data model"):
  with members:
    - "d4m bringing associative arrays to database engines"
    - "julia a fresh approach to numerical computing"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"
    - "julia a fast dynamic language for technical computing"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "graphulo linear algebra graph kernels for nosql databases"):
  with members:
    - "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-7 (induced by source paper "using a power law distribution to describe big data"):
  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)