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: "a sampling algebra for aggregate estimation"
  Abstract: As of 2005, sampling has been incorporated in all major database systems. While efficient sampling techniques are realizable, determining the accuracy of an estimate obtained from the sample is still an unresolved problem. In this paper, we present a theoretical framework that allows an elegant treatment of the problem. We base our work on generalized uniform sampling (GUS), a class of sampling methods that subsumes a wide variety of sampling techniques. We introduce a key notion of equivalence that allows GUS sampling operators to commute with selection and join, and derivation of confidence intervals. We illustrate the theory through extensive examples and give indications on how to use it to provide meaningful estimations in database systems.

Connected through hyperedge HE-1 (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"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "the big data bootstrap"
    - "early accurate results for advanced analytics on mapreduce"

Connected through hyperedge HE-2 (induced by source paper "infiniviz interactive visual exploration using progressive bin refinement"):
  with members:
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "visualization aware sampling for very large databases"

Connected through hyperedge HE-3 (induced by source paper "perfect and maximum randomness in stratified sampling over joins"):
  with members:
    - "pf ola a high performance framework for parallel on line aggregation"

Connected through hyperedge HE-4 (induced by source paper "stale view cleaning getting fresh answers from stale materialized views"):
  with members:
    - "linview incremental view maintenance for complex analytical queries"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

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)