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: "exploiting correlations for expensive predicate evaluation"
  Abstract: User Defined Function(UDFs) are used increasingly to augment query languages with extra, application dependent functionality. Selection queries involving UDF predicates tend to be expensive, either in terms of monetary cost or latency. In this paper, we study ways to efficiently evaluate selection queries with UDF predicates. We provide a family of techniques for processing queries at low cost while satisfying user-specified precision and recall constraints. Our techniques are applicable to a variety of scenarios including when selection probabilities of tuples are available beforehand, when this information is available but noisy, or when no such prior information is available. We also generalize our techniques to more complex queries. Finally, we test our techniques on real datasets, and show that they achieve significant savings in cost of up to $80\%$, while incurring only a small reduction in accuracy.

Connected through hyperedge HE-1 (induced by source paper "towards scalable dataframe systems"):
  with members:
    - "verdictdb universalizing approximate query processing"
    - "helix holistic optimization for accelerating iterative machine learning"
    - "ray a distributed framework for emerging ai applications"
    - "adaptive sampling for rapidly matching histograms"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "towards a holistic integration of spreadsheets with databases a scalable storage engine for presentational data management"
    - "lara a key value algebra underlying arrays and relations"
    - "visualization aware sampling for very large databases"

Connected through hyperedge HE-2 (induced by source paper "learning to sample counting with complex queries"):
  with members:
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-3 (induced by source paper "progressive evaluation of queries over untagged data"):
  with members:
    - "an idea an ingestion framework for data enrichment in asterixdb"
    - "verdictdb universalizing approximate query processing"
    - "sampling based query re optimization"
    - "meta des"

Connected through hyperedge HE-4 (induced by source paper "pique progressive integrated query operator with pay as you go enrichment"):
  with members:
    - "an idea an ingestion framework for data enrichment in asterixdb"

Connected through hyperedge HE-5 (induced by source paper "probably approximately optimal query optimization"):
  with members:
    - "human powered sorts and joins"

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)