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 "efficient fully compressed sequence representations"):
  Abstract: We present a data structure that stores a sequence $s[1..n]$ over alphabet $[1..\sigma]$ in $n\Ho(s) + o(n)(\Ho(s){+}1)$ bits, where $\Ho(s)$ is the zero-order entropy of $s$. This structure supports the queries \access, \rank\ and \select, which are fundamental building blocks for many other compressed data structures, in worst-case time $\Oh{\lg\lg\sigma}$ and average time $\Oh{\lg \Ho(s)}$. The worst-case complexity matches the best previous results, yet these had been achieved with data structures using $n\Ho(s)+o(n\lg\sigma)$ bits. On highly compressible sequences the $o(n\lg\sigma)$ bits of the redundancy may be significant compared to the the $n\Ho(s)$ bits that encode the data. Our representation, instead, compresses the redundancy as well. Moreover, our average-case complexity is unprecedented. Our technique is based on partitioning the alphabet into characters of similar frequency. The subsequence corresponding to each group can then be encoded using fast uncompressed representations without harming the overall compression ratios, even in the redundancy. The result also improves upon the best current compressed representations of several other data structures. For example, we achieve $(i)$ compressed redundancy, retaining the best time complexities, for the smallest existing full-text self-indexes; $(ii)$ compressed permutations $\pi$ with times for $\pi()$ and $\pii()$ improved to loglogarithmic; and $(iii)$ the first compressed representation of dynamic collections of disjoint sets. We also point out various applications to inverted indexes, suffix arrays, binary relations, and data compressors. ...

Includes members:
  Member "optimal dynamic sequence representations":
    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 "new lower and upper bounds for representing sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "run compressed rank select for large alphabets")
  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 "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 "optimal trade off for succinct string indexes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grammar compressed sequences with rank select support")
  Member "compressed representations of permutations and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compression in the space of permutations")
  Member "more haste less waste lowering the redundancy in fully indexable dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "four dimensional dominance range reporting in linear space")
  Member "a lower bound for succinct rank queries":
    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)