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 "breaking the variance approximating the hamming distance in tilde o 1 epsilon time per alignment"):
  Abstract: The algorithmic tasks of computing the Hamming distance between a given pattern of length $m$ and each location in a text of length $n$ is one of the most fundamental algorithmic tasks in string algorithms. Unfortunately, there is evidence that for a text $T$ of size $n$ and a pattern $P$ of size $m$, one cannot compute the exact Hamming distance for all locations in $T$ in time which is less than $\tilde O(n\sqrt m)$. However, Karloff~\cite{karloff} showed that if one is willing to suffer a $1\pm\epsilon$ approximation, then it is possible to solve the problem with high probability, in $\tilde O(\frac n {\epsilon^2})$ time. #R##N#Due to related lower bounds for computing the Hamming distance of two strings in the one-way communication complexity model, it is strongly believed that obtaining an algorithm for solving the approximation version cannot be done much faster as a function of $\frac 1 \epsilon$. We show here that this belief is false by introducing a new $\tilde O(\frac{n}{\epsilon})$ time algorithm that succeeds with high probability. #R##N#The main idea behind our algorithm, which is common in sparse recovery problems, is to reduce the variance of a specific randomized experiment by (approximately) separating heavy hitters from non-heavy hitters. However, while known sparse recovery techniques work very well on vectors, they do not seem to apply here, where we are dealing with mismatches between pairs of characters. We introduce two main algorithmic ingredients. The first is a new sparse recovery method that applies for pair inputs (such as in our setting). The second is a new construction of hash/projection functions, which allows to count the number of projections that induce mismatches between two characters exponentially faster than brute force. We expect that these algorithmic techniques will be of independent interest.

Includes members:
  Member "for all sparse recovery in near optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "edit distance cannot be computed in strongly subquadratic time unless seth is false":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "fast moment estimation in data streams in optimal space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "sublinear time measurement optimal sparse recovery for all":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "polylogarithmic approximation for edit distance and the asymmetric query complexity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "streaming with oracle new streaming algorithms for edit distance and lcs")
  Member "approximate sparse recovery optimizing time and measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy preserving learning via deep net pruning")

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)