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 "local decodability of the burrows wheeler transform"):
  Abstract: The Burrows-Wheeler Transform (BWT) is among the most influential discoveries in text compression and DNA storage. It is a reversible preprocessing step that rearranges an $n$-letter string into runs of identical characters (by exploiting context regularities), resulting in highly compressible strings, and is the basis of the \texttt{bzip} compression program. Alas, the decoding process of BWT is inherently sequential and requires $\Omega(n)$ time even to retrieve a \emph{single} character. #R##N#We study the succinct data structure problem of locally decoding short substrings of a given text under its \emph{compressed} BWT, i.e., with small additive redundancy $r$ over the \emph{Move-To-Front} (\texttt{bzip}) compression. The celebrated BWT-based FM-index (FOCS '00), as well as other related literature, yield a trade-off of $r=\tilde{O}(n/\sqrt{t})$ bits, when a single character is to be decoded in $O(t)$ time. We give a near-quadratic improvement $r=\tilde{O}(n\lg(t)/t)$. As a by-product, we obtain an \emph{exponential} (in $t$) improvement on the redundancy of the FM-index for counting pattern-matches on compressed text. In the interesting regime where the text compresses to $n^{1-o(1)}$ bits, these results provide an $\exp(t)$ \emph{overall} space reduction. For the local decoding problem of BWT, we also prove an $\Omega(n/t^2)$ cell-probe lower bound for "symmetric" data structures. #R##N#We achieve our main result by designing a compressed partial-sums (Rank) data structure over BWT. The key component is a \emph{locally-decodable} Move-to-Front (MTF) code: with only $O(1)$ extra bits per block of length $n^{\Omega(1)}$, the decoding time of a single character can be decreased from $\Omega(n)$ to $O(\lg n)$. This result is of independent interest in algorithmic information theory.

Includes members:
  Member "optimal time text indexing in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast indexes for gapped pattern matching")
  Member "fingerprints in compressed strings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lightweight fingerprints for fast approximate keyword matching using bitwise operations")
  Member "a simple online competitive adaptation of lempel ziv compression with efficient random access support":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs")
  Member "succinct representations of permutations and functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "path query data structures in practice")
  Member "cell probe lower bounds for prefix sums":
    also connected through other hyperedges:
      (none)

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)