human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "more haste less waste lowering the redundancy in fully indexable dictionaries"
  Abstract: We consider the problem of representing, in a compressed format, a bit-vector $S$ of $m$ bits with $n$ 1s, supporting the following operations, where $b \in \{0, 1 \}$: $rank_b(S,i)$ returns the number of occurrences of bit $b$ in the prefix $S[1..i]$; $select_b(S,i)$ returns the position of the $i$th occurrence of bit $b$ in $S$. Such a data structure is called \emph{fully indexable dictionary (FID)} [Raman et al.,2007], and is at least as powerful as predecessor data structures. Our focus is on space-efficient FIDs on the \textsc{ram} model with word size $\Theta(\lg m)$ and constant time for all operations, so that the time cost is independent of the input size. Given the bitstring $S$ to be encoded, having length $m$ and containing $n$ ones, the minimal amount of information that needs to be stored is $B(n,m) = \lceil \log {{m}\choose{n}} \rceil$. The state of the art in building a FID for $S$ is given in [Patrascu,2008] using $B(m,n)+O(m / ((\log m/ t) ^t)) + O(m^{3/4}) $ bits, to support the operations in $O(t)$ time. Here, we propose a parametric data structure exhibiting a time/space trade-off such that, for any real constants $0   0$, it uses \[ B(n,m) + O(n^{1+\delta} + n (\frac{m}{n^s})^\eps) \] bits and performs all the operations in time $O(s\delta^{-1} + \eps^{-1})$. The improvement is twofold: our redundancy can be lowered parametrically and, fixing $s = O(1)$, we get a constant-time FID whose space is $B(n,m) + O(m^\eps/\poly{n})$ bits, for sufficiently large $m$. This is a significant improvement compared to the previous bounds for the general case.

Connected through hyperedge HE-1 (induced by source paper "four dimensional dominance range reporting in linear space"):
  with members:
    - "succinct indices for range queries with applications to orthogonal range maxima"
    - "sorted range reporting"
    - "space efficient multi dimensional range reporting"

Connected through hyperedge HE-2 (induced by source paper "nearly optimal static las vegas succinct dictionary"):
  with members:
    - "how to store a random walk"
    - "optimal succinct rank data structure via approximate nonnegative tensor decomposition"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-3 (induced by source paper "path and ancestor queries on trees with multidimensional weight vectors"):
  with members:
    - "sorted range reporting"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-4 (induced by source paper "fast compressed self indexes with deterministic linear time construction"):
  with members:
    - "fast prefix search in little space with applications"
    - "linear time string indexing and analysis in small space"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "deterministic indexing for packed strings"
    - "linear time construction of compressed text indices in compact space"
    - "suffix trays and suffix trists structures for faster text indexing"
    - "alphabet dependent string searching with wexponential search trees"
    - "optimal lower and upper bounds for representing sequences"

Connected through hyperedge HE-5 (induced by source paper "linear time string indexing and analysis in small space"):
  with members:
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "a framework for space efficient string kernels"
    - "linear time construction of compressed text indices in compact space"
    - "linear time computation of minimal absent words using suffix array"
    - "optimal dynamic sequence representations"
    - "combined data structure for previous and next smaller values"
    - "optimal succinctness for range minimum queries"

Connected through hyperedge HE-6 (induced by source paper "space efficient construction of compressed indexes in deterministic linear time"):
  with members:
    - "linear time string indexing and analysis in small space"
    - "range predecessor and lempel ziv parsing"
    - "dynamic data structures for document collections and graphs"
    - "linear time construction of compressed text indices in compact space"
    - "optimal dynamic sequence representations"
    - "efficient fully compressed sequence representations"
    - "fully functional static and dynamic succinct trees"

Connected through hyperedge HE-7 (induced by source paper "range majorities and minorities in arrays"):
  with members:
    - "optimal encodings for range majority queries"
    - "optimal lower and upper bounds for representing sequences"
    - "dynamic range majority data structures"
    - "optimal succinctness for range minimum queries"
    - "searching for frequent colors in rectangles"
    - "range mode and range median queries on lists and trees"

Connected through hyperedge HE-8 (induced by source paper "linear time construction of compressed text indices in compact space"):
  with members:
    - "combined data structure for previous and next smaller values"
    - "optimal trade off for succinct string indexes"
    - "fully functional static and dynamic succinct trees"
    - "optimal succinctness for range minimum queries"

Question: Please tell me which class the center node 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)