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: "fast in memory xpath search over compressed text and tree indexes"
  Abstract: A large fraction of an XML document typically consists of text data. The XPath query language allows text search via the equal, contains, and starts-with predicates. Such predicates can efficiently be implemented using a compressed self-index of the document's text nodes. Most queries, however, contain some parts of querying the text of the document, plus some parts of querying the tree structure. It is therefore a challenge to choose an appropriate evaluation order for a given query, which optimally leverages the execution speeds of the text and tree indexes. Here the SXSI system is introduced; it stores the tree structure of an XML document using a bit array of opening and closing brackets, and stores the text nodes of the document using a global compressed self-index. On top of these indexes sits an XPath query engine that is based on tree automata. The engine uses fast counting queries of the text index in order to dynamically determine whether to evaluate top-down or bottom-up with respect to the tree structure. The resulting system has several advantages over existing systems: (1) on pure tree queries (without text search) such as the XPathMark queries, the SXSI system performs on par or better than the fastest known systems MonetDB and Qizx, (2) on queries that use text search, SXSI outperforms the existing systems by 1--3 orders of magnitude (depending on the size of the result set), and (3) with respect to memory consumption, SXSI outperforms all other systems for counting-only queries.

Connected through hyperedge HE-1 (induced by source paper "efficient compressed wavelet trees over large alphabets"):
  with members:
    - "optimal dynamic sequence representations"
    - "new lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "new algorithms on wavelet trees and applications to information retrieval"
    - "succinct representations of dynamic strings"
    - "efficient fully compressed sequence representations"
    - "range quantile queries another virtue of wavelet trees"
    - "fully functional static and dynamic succinct trees"

Connected through hyperedge HE-2 (induced by source paper "new lower and upper bounds for representing sequences"):
  with members:
    - "optimal trade off for succinct string indexes"
    - "efficient fully compressed sequence representations"
    - "compressed representations of permutations and applications"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "large alphabets and incompressibility"
    - "practical entropy compressed rank select dictionary"
    - "time space trade offs for predecessor search"

Connected through hyperedge HE-3 (induced by source paper "optimal lower and upper bounds for representing sequences"):
  with members:
    - "new lower and upper bounds for representing sequences"
    - "optimal trade off for succinct string indexes"
    - "efficient fully compressed sequence representations"
    - "compressed representations of permutations and applications"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "large alphabets and incompressibility"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-4 (induced by source paper "new algorithms on wavelet trees and applications to information retrieval"):
  with members:
    - "compressed representations of permutations and applications"
    - "range quantile queries another virtue of wavelet trees"
    - "towards optimal range medians"
    - "practical entropy compressed rank select dictionary"
    - "range mode and range median queries on lists and trees"

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)