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: "run compressed rank select for large alphabets"
  Abstract: Given a string of length $n$ that is composed of $r$ runs of letters from the alphabet $\{0,1,\ldots,\sigma{-}1\}$ such that $2 \le \sigma \le r$, we describe a data structure that, provided $r \le n / \log^{\omega(1)} n$, stores the string in $r\log\frac{n\sigma}{r} + o(r\log\frac{n\sigma}{r})$ bits and supports select and access queries in $O(\log\frac{\log(n/r)}{\log\log n})$ time and rank queries in $O(\log\frac{\log(n\sigma/r)}{\log\log n})$ time. We show that $r\log\frac{n(\sigma-1)}{r}$ bits are necessary for any such data structure and, thus, our solution is succinct. We also describe a data structure that uses $(1 + \epsilon)r\log\frac{n\sigma}{r} + O(r)$ bits, where $\epsilon > 0$ is an arbitrary constant, with the same query times but without the restriction $r \le n / \log^{\omega(1)} n$. By simple reductions to the colored predecessor problem, we show that the query times are optimal in the important case $r \ge 2^{\log^\delta n}$, for an arbitrary constant $\delta > 0$. We implement our solution and compare it with the state of the art, showing that the closest competitors consume 31-46% more space.

Connected through hyperedge HE-1 (induced by source paper "about birds project bioinformatics and information retrieval data structures analysis and design"):
  with members:
    - "compact representations of event sequences"
    - "parallel construction of wavelet trees on multicore architectures"
    - "efficient compression and indexing of trajectories"
    - "grammar compressed sequences with rank select support"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "aggregated 2d range queries on clustered points"
    - "navigational rule derivation an algorithm to determine the effect of traffic signs on road networks"

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)