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: "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.

Connected through hyperedge HE-1 (induced by source paper "rpair rescaling repair with rsync"):
  with members:
    - "gract a grammar based compressed index for trajectory data"
    - "mr repair grammar compression based on maximal repeats"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "a really simple approximation of smallest grammar"
    - "approximation of grammar based compression via recompression"

Connected through hyperedge HE-2 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "faster repetition aware compressed suffix trees based on block trees"
    - "optimal time dictionary compressed indexes"
    - "fast prefix search in little space with applications"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "representing the suffix tree with the cdawg"

Connected through hyperedge HE-3 (induced by source paper "refining the r index"):
  with members:
    - "conversion from rlbwt to lz77"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "lower bounds for text indexing with mismatches and differences"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "from lz77 to the run length encoded burrows wheeler transform and back"
    - "a framework of dynamic data structures for string processing"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "optimal time text indexing in bwt runs bounded space"

Connected through hyperedge HE-4 (induced by source paper "efficient construction of a complete index for pan genomics read alignment"):
  with members:
    - "optimal time text indexing in bwt runs bounded space"
    - "kmc 2 fast and resource frugal k mer counting"
    - "searching and indexing genomic databases via kernelization"
    - "hybrid indexes for repetitive datasets"
    - "indexes of large genome collections on a pc"

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)