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: "early accurate results for advanced analytics on mapreduce"
  Abstract: Approximate results based on samples often provide the only way in which advanced analytical applications on very massive data sets can satisfy their time and resource constraints. Unfortunately, methods and tools for the computation of accurate early results are currently not supported in MapReduce-oriented systems although these are intended for `big data'. Therefore, we proposed and implemented a non-parametric extension of Hadoop which allows the incremental computation of early results for arbitrary work-flows, along with reliable on-line estimates of the degree of accuracy achieved so far in the computation. These estimates are based on a technique called bootstrapping that has been widely employed in statistics and can be applied to arbitrary functions and data distributions. In this paper, we describe our Early Accurate Result Library (EARL) for Hadoop that was designed to minimize the changes required to the MapReduce framework. Various tests of EARL of Hadoop are presented to characterize the frequent situations where EARL can provide major speed-ups over the current version of Hadoop.

Connected through hyperedge HE-1 (induced by source paper "hillview a trillion cell spreadsheet for big data"):
  with members:
    - "big data visualization tools"
    - "foresight recommending visual insights"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rapid sampling for visualizations with ordering guarantees"
    - "a tutorial on principal component analysis"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"

Connected through hyperedge HE-2 (induced by source paper "a taxonomy on big data survey"):
  with members:
    - "mapreduce scheduler a 360 degree view"
    - "the family of mapreduce and large scale data processing systems"
    - "internet of things iot a vision architectural elements and future directions"
    - "restore reusing results of mapreduce jobs"

Connected through hyperedge HE-3 (induced by source paper "verdictdb universalizing approximate query processing"):
  with members:
    - "database learning toward a database that becomes smarter every time"
    - "visualization aware sampling for very large databases"
    - "approximate lifted inference with probabilistic databases"
    - "a sampling algebra for aggregate estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "the big data bootstrap"

Connected through hyperedge HE-4 (induced by source paper "taxonomy of big data a survey"):
  with members:
    - "mapreduce scheduler a 360 degree view"
    - "mllib machine learning in apache spark"
    - "the family of mapreduce and large scale data processing systems"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-5 (induced by source paper "accurateml information aggregation based approximate processing for fast and accurate machine learning on mapreduce"):
  with members:
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-6 (induced by source paper "10 millisecond computing"):
  with members:
    - "on horizontal decomposition of the operating system"
    - "bigdatabench a big data benchmark suite from internet services"
    - "when do redundant requests reduce latency"

Connected through hyperedge HE-7 (induced by source paper "empirical big data research a systematic literature mapping"):
  with members:
    - "performance evaluation of a natural language processing approach applied in white collar crime investigation"
    - "performance benefits of datampi a case study with bigdatabench"
    - "multilevel hierarchical kernel spectral clustering for real life large scale complex networks"
    - "graphical evolutionary game for information diffusion over social networks"
    - "the family of mapreduce and large scale data processing systems"
    - "optimizing i o for big array analytics"
    - "pf ola a high performance framework for parallel on line aggregation"
    - "can the elephants handle the nosql onslaught"

Connected through hyperedge HE-8 (induced by source paper "speculative approximations for terascale analytics"):
  with members:
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "dimmwitted a study of main memory statistical analytics"
    - "mli an api for distributed machine learning"
    - "the madlib analytics library or mad skills the sql"
    - "towards a unified architecture for in rdbms analytics"
    - "a reliable effective terascale linear learning system"
    - "optimal distributed online prediction using mini batches"

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)