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: "the vc dimension of queries and selectivity estimation through sampling"
  Abstract: We develop a novel method, based on the statistical concept of the Vapnik-Chervonenkis dimension, to evaluate the selectivity (output cardinality) of SQL queries - a crucial step in optimizing the execution of large scale database and data-mining operations. The major theoretical contribution of this work, which is of independent interest, is an explicit bound to the VC-dimension of a range space defined by all possible outcomes of a collection (class) of queries. We prove that the VC-dimension is a function of the maximum number of Boolean operations in the selection predicate and of the maximum number of select and join operations in any individual query in the collection, but it is neither a function of the number of queries in the collection nor of the size (number of tuples) of the database. We leverage on this result and develop a method that, given a class of queries, builds a concise random sample of a database, such that with high probability the execution of any query in the class on the sample provides an accurate estimate for the selectivity of the query on the original large database. The error probability holds simultaneously for the selectivity estimates of all queries in the collection, thus the same sample can be used to evaluate the selectivity of multiple queries, and the sample needs to be refreshed only following major changes in the database. The sample representation computed by our method is typically sufficiently small to be stored in main memory. We present extensive experimental results, validating our theoretical analysis and demonstrating the advantage of our technique when compared to complex selectivity estimation techniques used in PostgreSQL and the Microsoft SQL Server.

Connected through hyperedge HE-1 (induced by source paper "quicksel quick selectivity learning with mixture models"):
  with members:
    - "differentially private hierarchical count of counts histograms"
    - "the case for learned index structures"
    - "database learning toward a database that becomes smarter every time"
    - "building wavelet histograms on large data in mapreduce"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "k histograms an efficient clustering algorithm for categorical dataset"

Connected through hyperedge HE-2 (induced by source paper "vizrec a framework for secure data exploration via visual representation"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "data polygamy the many many relationships among urban spatio temporal data sets"
    - "controlling false discoveries during interactive data exploration"
    - "rapid sampling for visualizations with ordering guarantees"

Connected through hyperedge HE-3 (induced by source paper "research directions for principles of data management dagstuhl perspectives workshop 16151"):
  with members:
    - "size bounds and query plans for relational joins"
    - "a survey on measuring indirect discrimination in machine learning"
    - "query rewriting and optimization for ontological databases"
    - "communication steps for parallel query processing"
    - "probabilistic databases with markoviews"

Connected through hyperedge HE-4 (induced by source paper "epsilon samples of kernels"):
  with members:
    - "constructive discrepancy minimization by walking on the edges"
    - "structure aware sampling flexible and accurate summarization"
    - "constructive algorithms for discrepancy minimization"
    - "algorithms for eps approximations of terrains"

Connected through hyperedge HE-5 (induced by source paper "efficient discovery of association rules and frequent itemsets through sampling with tight performance guarantees"):
  with members:
    - "a learning theory approach to non interactive database privacy"
    - "tell me what i need to know succinctly summarizing data with itemsets"
    - "mining top k frequent itemsets through progressive sampling"
    - "shape fitting on point sets with probability distributions"

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)