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 and compact representations of prefix codes"):
  Abstract: Most of the attention in statistical compression is given to the space used by the compressed sequence, a problem completely solved with optimal prefix codes. However, in many applications, the storage space used to represent the prefix code itself can be an issue. In this paper we introduce and compare several techniques to store prefix codes. Let $N$ be the sequence length and $n$ be the alphabet size. Then a naive storage of an optimal prefix code uses $O(n\log n)$ bits. Our first technique shows how to use $O(n\log\log(N/n))$ bits to store the optimal prefix code. Then we introduce an approximate technique that, for any $0  1$, $O(n^{1 / c} \log n)$ bits to store a prefix code with average codeword length at most $c$ times the minimum. In all cases, our data structures allow encoding and decoding of any symbol in $O(1)$ time. We experimentally compare our new techniques with the state of the art, showing that we achieve 6--8-fold space reductions, at the price of a slower encoding (2.5--8 times slower) and decoding (12--24 times slower). The approximations further reduce this space and improve the time significantly, up to recovering the speed of classical implementations, for a moderate penalty in the average code length. As a byproduct, we compare various heuristic, approximate, and optimal algorithms to generate length-restricted codes, showing that the optimal ones are clearly superior and practical enough to be implemented.

Includes members:
  Member "new lower and upper bounds for representing sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "on adaptive algorithms for maximum matching")
  Member "fixed block compression boosting in fm indexes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressed multiple pattern matching")
  Member "compressed representations of permutations and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compression in the space of permutations")
  Member "fast and compact prefix codes":
    also connected through other hyperedges:
      (none)
  Member "worst case optimal adaptive prefix coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient fully compressed sequence representations")
  Member "dynamic asymmetric communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "new algorithms and lower bounds for sequential access data compression")
  Member "large alphabets and incompressibility":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lempel ziv like parsing in small space")

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)