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 "prefix free parsing for building big bwts"):
  Abstract: High-throughput sequencing technologies have led to explosive growth of genomic databases, the largest of which will soon be hundreds of terabytes or more. For many applications we want indexes of these databases but building them is a challenge. Fortunately, many genomic databases are highly repetitive and we should be able to use that to help us compute the Burrows-Wheeler Transform (BWT), which underlies many popular indexes. In this paper we describe a preprocessing step, {\em prefix-free parsing}, that takes a text $T$ and in one pass generates a dictionary $D$ and a parse $P$ of $T$, with the property that we can compute the BWT of $T$ from $D$ and $P$ alone using workspace proportional only to their total size, and $O (|T|)$ time when we can work in internal memory. Our experiments show that $D$ and $P$ are often significantly smaller than $T$ in practice and so may fit in a reasonable internal memory even when $T$ is very large.

Includes members:
  Member "from lz77 to the run length encoded burrows wheeler transform and back":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refining the r index")
  Member "optimal time text indexing in bwt runs bounded space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast indexes for gapped pattern matching")
  Member "burrows wheeler transform for terabases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightweight merging of compressed indices based on bwt variants")
  Member "from theory to practice plug and play with succinct data structures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bitvectors with runs and the successor predecessor problem")
  Member "lightweight data indexing and compression in external memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "external memory bwt and lcp computation for sequence collections with applications")

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)