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 "how to store a random walk"):
  Abstract: Motivated by storage applications, we study the following data structure problem: An encoder wishes to store a collection of jointly-distributed files $\overline{X}:=(X_1,X_2,\ldots, X_n) \sim \mu$ which are \emph{correlated} ($H_\mu(\overline{X}) \ll \sum_i H_\mu(X_i)$), using as little (expected) memory as possible, such that each individual file $X_i$ can be recovered quickly with few (ideally constant) memory accesses. #R##N#In the case of independent random files, a dramatic result by \Pat (FOCS'08) and subsequently by Dodis, \Pat and Thorup (STOC'10) shows that it is possible to store $\overline{X}$ using just a \emph{constant} number of extra bits beyond the information-theoretic minimum space, while at the same time decoding each $X_i$ in constant time. However, in the (realistic) case where the files are correlated, much weaker results are known, requiring at least $\Omega(n/poly\lg n)$ extra bits for constant decoding time, even for "simple" joint distributions $\mu$. #R##N#We focus on the natural case of compressing\emph{Markov chains}, i.e., storing a length-$n$ random walk on any (possibly directed) graph $G$. Denoting by $\kappa(G,n)$ the number of length-$n$ walks on $G$, we show that there is a succinct data structure storing a random walk using $\lg_2 \kappa(G,n) + O(\lg n)$ bits of space, such that any vertex along the walk can be decoded in $O(1)$ time on a word-RAM. For the harder task of matching the \emph{point-wise} optimal space of the walk, i.e., the empirical entropy $\sum_{i=1}^{n-1} \lg (deg(v_i))$, we present a data structure with $O(1)$ extra bits at the price of $O(\lg n)$ decoding time, and show that any improvement on this would lead to an improved solution on the long-standing Dictionary problem. All of our data structures support the \emph{online} version of the problem with constant update and query time.

Includes members:
  Member "local decodability of the burrows wheeler transform":
    also connected through other hyperedges:
      (none)
  Member "on locally decodable source coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "o log log n worst case local decoding and update efficiency for data compression")
  Member "a simple online competitive adaptation of lempel ziv compression with efficient random access support":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs")
  Member "on compressing permutations and adaptive sorting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on adaptive algorithms for maximum matching")
  Member "the cell probe complexity of dynamic range counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tight static lower bounds for non adaptive data structures")
  Member "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "path query data structures in practice")

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)