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: "optimal joins using compact data structures"
  Abstract: Worst-case optimal join algorithms have gained a lot of attention in the database literature. We now count with several different algorithms that have all been shown to be optimal in the worst case, and many of them have also been implemented and tested in practice. However, the implementation of these algorithms often requires an enhanced indexing structure: to achieve optimality we either need to build completely new indexes or we must populate the database with several different instantiations of common indexes such as B+-trees. Either way, this means spending an extra amount of storage space that may be non-negligible. #R##N#In this paper we show that optimal algorithms can be obtained directly from a representation that regards the relations as point sets in variable-dimensional grids, without the need of extra storage. Our representation is a compact quadtree for the static indexes and a dynamic quadtree sharing subtrees (which we dub a Qdag) for intermediate results. We develop a compositional algorithm to process full join queries when data is stored in said structures, and then show that the running time of this algorithm is worst-case optimal in data complexity. #R##N#Remarkably, we can even extend our framework to compute more expressive queries in relational algebra using both unions and a form of limited negation, by introducing a lazy version of Qdags. Once again, we can show that the running time of our algorithms is worst-case optimal.

Connected through hyperedge HE-1 (induced by source paper "worst case optimal radix triejoin"):
  with members:
    - "counting triangles under updates in worst case optimal time"
    - "constraint solving via fractional edge covers"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "skew strikes back new developments in the theory of join algorithms"

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)