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 "full text and keyword indexes for string searching"):
  Abstract: In this work, we present a literature review for full-text and keyword indexes as well as our contributions (which are mostly practice-oriented). #R##N#The first contribution is the FM-bloated index, which is a modification of the well-known FM-index (a compressed, full-text index) that trades space for speed. In our approach, the count table and the occurrence lists store information about selected $q$-grams in addition to the individual characters. Two variants are described, namely one using $O(n \log^2 n)$ bits of space with $O(m + \log m \log \log n)$ average query time, and one with linear space and $O(m \log \log n)$ average query time, where $n$ is the input text length and $m$ is the pattern length. We experimentally show that a significant speedup can be achieved by operating on $q$-grams (albeit at the cost of very high space requirements, hence the name "bloated"). #R##N#In the category of keyword indexes we present the so-called split index, which can efficiently solve the $k$-mismatches problem, especially for 1 error. Our implementation in the C++ language is focused mostly on data compaction, which is beneficial for the search speed (by being cache friendly). We compare our solution with other algorithms and we show that it is faster when the Hamming distance is used. Query times in the order of 1 microsecond were reported for one mismatch for a few-megabyte natural language dictionary on a medium-end PC. #R##N#A minor contribution includes string sketches which aim to speed up approximate string comparison at the cost of additional space ($O(1)$ per string). They can be used in the context of keyword indexes in order to deduce that two strings differ by at least $k$ mismatches with the use of fast bitwise operations rather than an explicit verification.

Includes members:
  Member "the k mismatch problem revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating text to pattern hamming distances")
  Member "a practical index for approximate dictionary matching with few mismatches":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a bloated fm index reducing the number of cache misses during the search")
  Member "new tabulation and sparse dynamic programming based techniques for sequence similarity problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating lcs in linear time beating the sqrt n barrier")
  Member "from theory to practice plug and play with succinct data structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bitvectors with runs and the successor predecessor problem")
  Member "compressed text indexes from theory to practice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a compact representation for trips over networks built on self indexes")

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)