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: "skyquery an implementation of a parallel probabilistic join engine for cross identification of multiple astronomical databases"
  Abstract: Multi-wavelength astronomical studies require cross-identification of detections of the same celestial objects in multiple catalogs based on spherical coordinates and other properties. Because of the large data volumes and spherical geometry, the symmetric N-way association of astronomical detections is a computationally intensive problem, even when sophisticated indexing schemes are used to exclude obviously false candidates. Legacy astronomical catalogs already contain detections of more than a hundred million objects while the ongoing and future surveys will produce catalogs of billions of objects with multiple detections of each at different times. The varying statistical error of position measurements, moving and extended objects, and other physical properties make it necessary to perform the cross-identification using a mathematically correct, proper Bayesian probabilistic algorithm, capable of including various priors. One time, pair-wise cross-identification of these large catalogs is not sufficient for many astronomical scenarios. Consequently, a novel system is necessary that can cross-identify multiple catalogs on-demand, efficiently and reliably. In this paper, we present our solution based on a cluster of commodity servers and ordinary relational databases. The cross-identification problems are formulated in a language based on SQL, but extended with special clauses. These special queries are partitioned spatially by coordinate ranges and compiled into a complex workflow of ordinary SQL queries. Workflows are then executed in a parallel framework using a cluster of servers hosting identical mirrors of the same data sets.

Connected through hyperedge HE-1 (induced by source paper "a multi terabyte relational database for geo tagged social network data"):
  with members:
    - "spatial indexing of large multidimensional databases"
    - "array requirements for scientific applications and an implementation for microsoft sql server"
    - "indexing the sphere with the hierarchical triangular mesh"
    - "batch is back casjobs serving multi tb data on the web"

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)