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: "packed compact tries a fast and efficient data structure for online string processing"
  Abstract: In this paper, we present a new data structure called the packed compact trie (packed c-trie) which stores a set $S$ of $k$ strings of total length $n$ in $n \log\sigma + O(k \log n)$ bits of space and supports fast pattern matching queries and updates, where $\sigma$ is the size of an alphabet. Assume that $\alpha = \log_\sigma n$ letters are packed in a single machine word on the standard word RAM model, and let $f(k,n)$ denote the query and update times of the dynamic predecessor/successor data structure of our choice which stores $k$ integers from universe $[1,n]$ in $O(k \log n)$ bits of space. Then, given a string of length $m$, our packed c-tries support pattern matching queries and insert/delete operations in $O(\frac{m}{\alpha} f(k,n))$ worst-case time and in $O(\frac{m}{\alpha} + f(k,n))$ expected time. Our experiments show that our packed c-tries are faster than the standard compact tries (a.k.a. Patricia trees) on real data sets. As an application of our packed c-trie, we show that the sparse suffix tree for a string of length $n$ over prefix codes with $k$ sampled positions, such as evenly-spaced and word delimited sparse suffix trees, can be constructed online in $O((\frac{n}{\alpha} + k) f(k,n))$ worst-case time and $O(\frac{n}{\alpha} + k f(k,n))$ expected time with $n \log \sigma + O(k \log n)$ bits of space. When $k = O(\frac{n}{\alpha})$, by using the state-of-the-art dynamic predecessor/successor data structures, we obtain sub-linear time construction algorithms using only $O(\frac{n}{\alpha})$ bits of space in both cases. We also discuss an application of our packed c-tries to online LZD factorization.

Connected through hyperedge HE-1 (induced by source paper "top tree compression of tries"):
  with members:
    - "dynamic packed compact tries revisited"
    - "universal compressed text indexing"
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "deterministic indexing for packed strings"
    - "composite repetition aware data structures"
    - "tree compression with top trees revisited"

Connected through hyperedge HE-2 (induced by source paper "dynamic path decomposed tries"):
  with members:
    - "dynamic packed compact tries revisited"
    - "separate chaining meets compact hashing"
    - "m bonsai a practical compact dynamic trie"
    - "a framework of dynamic data structures for string processing"
    - "practical evaluation of lempel ziv 78 and lempel ziv welch tries"
    - "from theory to practice plug and play with succinct data structures"
    - "fast compressed tries through path decompositions"

Connected through hyperedge HE-3 (induced by source paper "separate chaining meets compact hashing"):
  with members:
    - "practical evaluation of lempel ziv 78 and lempel ziv welch tries"
    - "compression with the tudocomp framework"

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)