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 k mismatch with data recovery and applications"):
  Abstract: We revisit the k-mismatch problem, one of the most basic problems in pattern matching. Given a pattern of length m and a text of length n, the task is to find all m-length substrings of the text that are at the Hamming distance at most $k$ from the pattern. Our first contribution is two new streaming algorithms for the k-mismatch problem. #R##N#1) For $k = 1$, we give an $O(polylog{m})$-space algorithm that uses $O(polylog{m})$ worst-case time per arriving symbol; #R##N#2) For $k \ge 2$, the problem can be solved in $O(k^2 polylog{m})$ space and $O(k \, polylog{m})$ worst-case time per arriving symbol. #R##N#The complexities of our new k-mismatch algorithms match (up to logarithmic factors) those of the best known solutions from FOCS 2009 and SODA 2016, and our algorithms are enhanced with an important new feature which we call Data Recovery: For each alignment where the Hamming distance is at most k, the algorithms output the difference of symbols of the pattern and of the text in the mismatching positions. This is a particularly surprising feature as we are not allowed to store a copy of the pattern or the text in the streaming setting. #R##N#Armed with this new feature, we develop the first streaming algorithms for pattern matching on weighted strings. Unlike regular strings, a weighted string is not a sequence of symbols but rather a sequence of probability distributions on the alphabet. In the Weighted Pattern Matching problem we are given a text and a pattern, both of which can be either weighted or regular strings, and must find all alignments of the text and of the pattern where they match with probability above a given threshold. #R##N#The algorithms presented in the paper are randomised and correct with high probability.

Includes members:
  Member "pattern matching and consensus problems on weighted sequences and profiles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weighted shortest common supersequence problem revisited")
  Member "approximate hamming distance in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating text to pattern hamming distances")
  Member "dictionary matching in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sliding window property testing for regular languages")
  Member "the k mismatch problem revisited":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating text to pattern hamming distances")
  Member "probabilistic threshold indexing for uncertain strings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "indexing weighted sequences neat and efficient")
  Member "space lower bounds for online pattern matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "periodicity in data streams with wildcards")

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)