human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compressed multiple pattern matching"):
  Abstract: Given $d$ strings over the alphabet $\{0,1,\ldots,\sigma{-}1\}$, the classical Aho--Corasick data structure allows us to find all $occ$ occurrences of the strings in any text $T$ in $O(|T| + occ)$ time using $O(m\log m)$ bits of space, where $m$ is the number of edges in the trie containing the strings. Fix any constant $\varepsilon \in (0, 2)$. We describe a compressed solution for the problem that, provided $\sigma \le m^\delta$ for a constant $\delta < 1$, works in $O(|T| \frac{1}{\varepsilon} \log\frac{1}{\varepsilon} + occ)$ time, which is $O(|T| + occ)$ since $\varepsilon$ is constant, and occupies $mH_k + 1.443 m + \varepsilon m + O(d\log\frac{m}{d})$ bits of space, for all $0 \le k \le \max\{0,\alpha\log_\sigma m - 2\}$ simultaneously, where $\alpha \in (0,1)$ is an arbitrary constant and $H_k$ is the $k$th-order empirical entropy of the trie. Hence, we reduce the $3.443m$ term in the space bounds of previously best succinct solutions to $(1.443 + \varepsilon)m$, thus solving an open problem posed by Belazzougui. Further, we notice that $L = \log\binom{\sigma (m+1)}{m} - O(\log(\sigma m))$ is a worst-case space lower bound for any solution of the problem and, for $d = o(m)$ and constant $\varepsilon$, our approach allows to achieve $L + \varepsilon m$ bits of space, which gives an evidence that, for $d = o(m)$, the space of our data structure is theoretically optimal up to the $\varepsilon m$ additive term and it is hardly possible to eliminate the term $1.443m$. In addition, we refine the space analysis of previous works by proposing a more appropriate definition for $H_k$. We also simplify the construction for practice adapting the fixed block compression boosting technique, then implement our data structure, and conduct a number of experiments showing that it is comparable to the state of the art in terms of time and is superior in space.

Includes members:
  Member "greedy shortest common superstring approximation in compact space":
    also connected through other hyperedges:
      (none)
  Member "dictionary matching in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sliding window property testing for regular languages")
  Member "engineering small space dictionary matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully functional bidirectional burrows wheeler indexes")
  Member "fixed block compression boosting in fm indexes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling")
  Member "succinct dictionary matching with no slowdown":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the hardness and inapproximability of recognizing wheeler graphs")
  Member "large alphabets and incompressibility":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lempel ziv like parsing in small space")

Question: Please tell me which class the source paper 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)