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: "alphabet dependent string searching with wexponential search trees"
  Abstract: It is widely assumed that $O(m+\lg \sigma)$ is the best one can do for finding a pattern of length $m$ in a compacted trie storing strings over an alphabet of size $\sigma$, if one insists on linear-size data structures and deterministic worst-case running times [Cole et al., ICALP'06]. In this article, we first show that a rather straightforward combination of well-known ideas yields $O(m+\lg\lg \sigma)$ deterministic worst-case searching time for static tries. #R##N#Then we move on to dynamic tries, where we achieve a worst-case bound of $O(m+\frac{\lg^{2}\lg\sigma}{\lg\lg\lg\sigma})$ per query or update, which should again be compared to the previously known $O(m+\lg\sigma)$ deterministic worst-case bounds [Cole et al., ICALP'06], and to the alphabet \emph{in}dependent $O(m+\sqrt{\lg n/\lg\lg n})$ deterministic worst-case bounds [Andersson and Thorup, SODA'01], where $n$ is the number of nodes in the trie. The basis of our update procedure is a weighted variant of exponential search trees which, while simple, might be of independent interest. #R##N#As one particular application, the above bounds (static and dynamic) apply to suffix trees. There, an update corresponds to pre- or appending a letter to the text, and an additional goal is to do the updates quicker than rematching entire suffixes. We show how to do this in $O(\lg\lg n + \frac{\lg^{2}\lg\sigma}{\lg\lg\lg\sigma})$ time, which improves the previously known $O(\lg n)$ bound [Amir et al., SPIRE'05].

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"
    - "streamed graph drawing and the file maintenance problem"
    - "on line indexing for general alphabets via predecessor queries on subsets of an ordered list"

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"
    - "streamed graph drawing and the file maintenance problem"
    - "on line indexing for general alphabets via predecessor queries on subsets of an ordered list"

Connected through hyperedge HE-3 (induced by source paper "suffix trees dawgs and cdawgs for forward and backward tries"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "fast label extraction in the cdawg"
    - "faster compact on line lempel ziv factorization"
    - "fast computation of subpath kernel for trees"

Connected through hyperedge HE-4 (induced by source paper "online algorithms for constructing linear size suffix trie"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "fast label extraction in the cdawg"
    - "optimal dynamic strings"
    - "on line construction of position heaps"

Connected through hyperedge HE-5 (induced by source paper "conversion from rlbwt to lz77"):
  with members:
    - "repair in compressed space and time"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "dynamic relative compression dynamic partial sums and substring concatenation"
    - "a really simple approximation of smallest grammar"

Connected through hyperedge HE-6 (induced by source paper "fast compressed self indexes with deterministic linear time construction"):
  with members:
    - "fast prefix search in little space with applications"
    - "linear time string indexing and analysis in small space"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "deterministic indexing for packed strings"
    - "linear time construction of compressed text indices in compact space"
    - "suffix trays and suffix trists structures for faster text indexing"
    - "optimal lower and upper bounds for representing sequences"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

Connected through hyperedge HE-7 (induced by source paper "text indexing and searching in sublinear time"):
  with members:
    - "orthogonal point location and rectangle stabbing queries in 3 d"
    - "fast compressed self indexes with deterministic linear time construction"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "sparse suffix tree construction in optimal time and space"
    - "deterministic indexing for packed strings"
    - "range predecessor and lempel ziv parsing"
    - "wavelet trees meet suffix trees"
    - "suffix trays and suffix trists structures for faster text indexing"

Connected through hyperedge HE-8 (induced by source paper "practical evaluation of lempel ziv 78 and lempel ziv welch tries"):
  with members:
    - "dynamic space efficient hashing"
    - "compression with the tudocomp framework"
    - "faster 64 bit universal hashing using carry less multiplications"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "range predecessor and lempel ziv parsing"
    - "lempel ziv computation in compressed space lz cics"
    - "lightweight lempel ziv parsing"
    - "lz compressed string dictionaries"

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)