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: "set containment join revisited"
  Abstract: Given two collections of set objects R and S, the $$R\bowtie _{\subseteq }S$$Rź⊆S set containment join returns all object pairs $$(r,s) \in R\times S$$(r,s)źR×S such that $$r\subseteq s$$r⊆s. Besides being a basic operator in all modern data management systems with a wide range of applications, the join can be used to evaluate complex SQL queries based on relational division and as a module of data mining algorithms. The state-of-the-art algorithm for set containment joins ($$\mathtt {PRETTI}$$PRETTI) builds an inverted index on the right-hand collection S and a prefix tree on the left-hand collection R that groups set objects with common prefixes and thus, avoids redundant processing. In this paper, we present a framework which improves $$\mathtt {PRETTI}$$PRETTI in two directions. First, we limit the prefix tree construction by proposing an adaptive methodology based on a cost model; this way, we can greatly reduce the space and time cost of the join. Second, we partition the objects of each collection based on their first contained item, assuming that the set objects are internally sorted. We show that we can process the partitions and evaluate the join while building the prefix tree and the inverted index progressively. This allows us to significantly reduce not only the join cost, but also the maximum memory requirements during the join. An experimental evaluation using both real and synthetic datasets shows that our framework outperforms $$\mathtt {PRETTI}$$PRETTI by a wide margin.

Connected through hyperedge HE-1 (induced by source paper "fast join project query evaluation using matrix multiplication"):
  with members:
    - "memory efficient group by aggregates over multi way joins"
    - "trade offs in static and dynamic evaluation of hierarchical queries"
    - "size bounds and query plans for relational joins"
    - "extracting and analyzing hidden graphs from relational databases"
    - "compressed representations of conjunctive query results"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "data structure lower bounds for document indexing problems"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-2 (induced by source paper "gb kmv an augmented kmv sketch for approximate containment similarity search"):
  with members:
    - "silkmoth an efficient method for finding related sets with maximum matching constraints"
    - "fast similarity sketching"
    - "lsh ensemble internet scale domain search"
    - "set similarity search beyond minhash"
    - "a minimal variance estimator for the cardinality of big data set intersection"

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)