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 "approximating text to pattern hamming distances"):
  Abstract: We revisit a fundamental problem in string matching: given a pattern of length m and a text of length n, both over an alphabet of size $\sigma$, compute the Hamming distance between the pattern and the text at every location. Several $(1+\epsilon)$-approximation algorithms have been proposed in the literature, with running time of the form $O(\epsilon^{-O(1)}n\log n\log m)$, all using fast Fourier transform (FFT). We describe a simple $(1+\epsilon)$-approximation algorithm that is faster and does not need FFT. Combining our approach with additional ideas leads to numerous new results: #R##N#- We obtain the first linear-time approximation algorithm; the running time is $O(\epsilon^{-2}n)$. #R##N#- We obtain a faster exact algorithm computing all Hamming distances up to a given threshold k; its running time improves previous results by logarithmic factors and is linear if $k\le\sqrt m$. #R##N#- We obtain approximation algorithms with better $\epsilon$-dependence using rectangular matrix multiplication. The time-bound is $O(n)$ when the pattern is sufficiently long: $m\ge \epsilon^{-28}$. Previous algorithms require $O(\epsilon^{-1}n)$ time. #R##N#- When k is not too small, we obtain a truly sublinear-time algorithm to find all locations with Hamming distance approximately (up to a constant factor) less than k, in $O((n/k^{\Omega(1)}+occ)n^{o(1)})$ time, where occ is the output size. The algorithm leads to a property tester, returning true if an exact match exists and false if the Hamming distance is more than $\delta m$ at every location, running in $O(\delta^{-1/3}n^{2/3}+\delta^{-1}n/m)$ time. #R##N#- We obtain a streaming algorithm to report all locations with Hamming distance approximately less than k, using $O(\epsilon^{-2}\sqrt k)$ space. Previously, streaming algorithms were known for the exact problem with O(k) space or for the approximate problem with $O(\epsilon^{-O(1)}\sqrt m)$ space.

Includes members:
  Member "l p pattern matching in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating text to pattern distance via dimensionality reduction")
  Member "string synchronizing sets sublinear time bwt construction and optimal lce data structure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering")
  Member "the streaming k mismatch problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating text to pattern distance via dimensionality reduction")
  Member "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "parameter free locality sensitive hashing for spherical range reporting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algorithms for similarity search and pseudorandomness")
  Member "optimal hashing based time space trade offs for approximate near neighbors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "polynomial representations of threshold functions and algorithmic applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "algorithms and lower bounds for de morgan formulas of low communication leaf gates")
  Member "approximate hamming distance in a stream":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating text to pattern distance via dimensionality reduction")

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)