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 "rank and select another lesson learned"):
  Abstract: Rank and select queries on bitmaps are essential building bricks of many compressed data structures, including text indexes, membership and range supporting spatial data structures, compressed graphs, and more. Theoretically considered yet in 1980s, these primitives have also been a subject of vivid research concerning their practical incarnations in the last decade. We present a few novel rank/select variants, focusing mostly on speed, obtaining competitive space-time results in the compressed setting. Our findings can be summarized as follows: $(i)$ no single rank/select solution works best on any kind of data (ours are optimized for concatenated bit arrays obtained from wavelet trees for real text datasets), $(ii)$ it pays to efficiently handle blocks consisting of all 0 or all 1 bits, $(iii)$ compressed select does not have to be significantly slower than compressed rank at a comparable memory use.

Includes members:
  Member "a fast x86 implementation of select":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on slicing sorted integer sequences")
  Member "fm index for dummies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lisa towards learned dna sequence search")
  Member "from theory to practice plug and play with succinct data structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bitvectors with runs and the successor predecessor problem")
  Member "practical entropy compressed rank select dictionary":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bitvectors with runs and the successor predecessor problem")

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)