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 "streaming pattern matching with d wildcards"):
  Abstract: In the pattern matching with $d$ wildcards problem one is given a text $T$ of length $n$ and a pattern $P$ of length $m$ that contains $d$ wildcard characters, each denoted by a special symbol $'?'$. A wildcard character matches any other character. The goal is to establish for each $m$-length substring of $T$ whether it matches $P$. In the streaming model variant of the pattern matching with $d$ wildcards problem the text $T$ arrives one character at a time and the goal is to report, before the next character arrives, if the last $m$ characters match $P$ while using only $o(m)$ words of space. #R##N#In this paper we introduce two new algorithms for the $d$ wildcard pattern matching problem in the streaming model. The first is a randomized Monte Carlo algorithm that is parameterized by a constant $0\leq \delta \leq 1$. This algorithm uses $\tilde{O}(d^{1-\delta})$ amortized time per character and $\tilde{O}(d^{1+\delta})$ words of space. The second algorithm, which is used as a black box in the first algorithm, is a randomized Monte Carlo algorithm which uses $O(d+\log m)$ worst-case time per character and $O(d\log m)$ words of space.

Includes members:
  Member "the streaming k mismatch problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances")
  Member "dictionary matching in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sliding window property testing for regular languages")
  Member "the k mismatch problem revisited":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating text to pattern hamming distances")
  Member "space lower bounds for online pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "periodicity in data streams with wildcards")
  Member "parameterized matching in the streaming model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the streaming k mismatch problem")
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving tall dense linear programs in nearly linear time")

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)