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: "on line indexing for general alphabets via predecessor queries on subsets of an ordered list"
  Abstract: The problem of Text Indexing is a fundamental algorithmic problem in which one wishes to preprocess a text in order to quickly locate pattern queries within the text. In the ever evolving world of dynamic and on-line data, there is also a need for developing solutions to index texts which arrive on-line, i.e. a character at a time, and still be able to quickly locate said patterns. In this paper, a new solution for on-line indexing is presented by providing an on-line suffix tree construction in $O(\log \log n + \log\log |\Sigma|)$ worst-case expected time per character, where $n$ is the size of the string, and $\Sigma$ is the alphabet. This improves upon all previously known on-line suffix tree constructions for general alphabets, at the cost of having the run time in expectation. #R##N#The main idea is to reduce the problem of constructing a suffix tree on-line to an interesting variant of the order maintenance problem, which may be of independent interest. In the famous order maintenance problem, one wishes to maintain a dynamic list $L$ of size $n$ under insertions, deletions, and order queries. In an order query, one is given two nodes from $L$ and must determine which node precedes the other in $L$. In the Predecessor search on Dynamic Subsets of an Ordered Dynamic List problem (POLP) it is also necessary to maintain dynamic subsets of $L$ such that given some $u\in L$ it will be possible to quickly locate the predecessor of $u$ in any subset. This paper provides an efficient data structure capable of solving the POLP with worst-case expected bounds that match the currently best known bounds for predecessor search in the RAM model, improving over a solution which may be implicitly obtained from Dietz [Die89]. #R##N#Furthermore, this paper improves or simplifies bounds for several additional applications, including fully-persistent arrays and the Order-Maintenance Problem.

Connected through hyperedge HE-1 (induced by source paper "achieving optimal backlog in the vanilla multi processor cup game"):
  with members:
    - "achieving optimal backlog in multi processor cup games"
    - "the minimum backlog problem"
    - "alphabet dependent string searching with wexponential search trees"
    - "streamed graph drawing and the file maintenance problem"

Connected through hyperedge HE-2 (induced by source paper "achieving optimal backlog in multi processor cup games"):
  with members:
    - "optimal ball recycling"
    - "the minimum backlog problem"
    - "alphabet dependent string searching with wexponential search trees"
    - "streamed graph drawing and the file maintenance problem"

Connected through hyperedge HE-3 (induced by source paper "compressed data structures for dynamic sequences"):
  with members:
    - "optimal dynamic sequence representations"
    - "new lower and upper bounds for representing sequences"
    - "succinct representations of permutations and functions"
    - "cram compressed random access memory"
    - "succinct representations of dynamic strings"
    - "fully functional static and dynamic succinct trees"

Connected through hyperedge HE-4 (induced by source paper "alphabet dependent string searching with wexponential search trees"):
  with members:
    - "suffix trays and suffix trists structures for faster text indexing"
    - "full fledged real time indexing for constant size alphabets"
    - "dynamic ordered sets with exponential search trees"

Connected through hyperedge HE-5 (induced by source paper "full fledged real time indexing for constant size alphabets"):
  with members:
    - "alphabet dependent string searching with wexponential search trees"
    - "cross document pattern matching"

Connected through hyperedge HE-6 (induced by source paper "suffix trays and suffix trists structures for faster text indexing"):
  with members:
    - "orthogonal range searching for text indexing"

Connected through hyperedge HE-7 (induced by source paper "streamed graph drawing and the file maintenance problem"):
  with members:
    - "cole s parametric search technique made practical"

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.DS(Data Structures and Algorithms)