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: "large scale compression of genomic sequence databases with the burrows wheeler transform"
  Abstract: Motivation: The Burrows–Wheeler transform (BWT) is the foundation of many algorithms for compression and indexing of text data, but the cost of computing the BWT of very large string collections has prevented these techniques from being widely applied to the large sets of sequences often encountered as the outcome of DNA sequencing experiments. In previous work, we presented a novel algorithm that allows the BWT of human genome scale data to be computed on very moderate hardware, thus enabling us to investigate the BWT as a tool for the compression of such datasets.#R##N##R##N#Results: We first used simulated reads to explore the relationship between the level of compression and the error rate, the length of the reads and the level of sampling of the underlying genome and compare choices of second-stage compression algorithm.#R##N##R##N#We demonstrate that compression may be greatly improved by a particular reordering of the sequences in the collection and give a novel ‘implicit sorting’ strategy that enables these benefits to be realized without the overhead of sorting the reads. With these techniques, a 45× coverage of real human genome sequence data compresses losslessly to under 0.5 bits per base, allowing the 135.3 Gb of sequence to fit into only 8.2 GB of space (trimming a small proportion of low-quality bases from the reads improves the compression still further).#R##N##R##N#This is >4 times smaller than the size achieved by a standard BWT-based compressor (bzip2) on the untrimmed reads, but an important further advantage of our approach is that it facilitates the building of compressed full text indexes such as the FM-index on large-scale DNA sequence collections.#R##N##R##N#Availability: Code to construct the BWT and SAP-array on large genomic datasets is part of the BEETL library, available as a github repository at https://github.com/BEETL/BEETL.#R##N##R##N#Contact: [email protected]

Connected through hyperedge HE-1 (induced by source paper "local decode and update for big data compression"):
  with members:
    - "how to store a random walk"
    - "set membership with non adaptive bit probes"
    - "compressed data structures for dynamic sequences"
    - "set membership with a few bit probes"
    - "bounds on the parameters of locally recoverable codes"
    - "a family of optimal locally recoverable codes"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "on locally decodable source coding"

Connected through hyperedge HE-2 (induced by source paper "the alternating bwt an algorithmic perspective"):
  with members:
    - "a new class of searchable and provably highly compressible string transformations"
    - "on generalized lyndon words"
    - "external memory bwt and lcp computation for sequence collections with applications"
    - "lightweight lcp construction for very large collections of strings"
    - "optimal lower and upper bounds for representing sequences"
    - "on optimally partitioning a text to improve its compression"
    - "a note on the burrows wheeler transformation"

Connected through hyperedge HE-3 (induced by source paper "a new class of searchable and provably highly compressible string transformations"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "optimal time text indexing in bwt runs bounded space"
    - "optimal lower and upper bounds for representing sequences"
    - "a note on the burrows wheeler transformation"

Connected through hyperedge HE-4 (induced by source paper "burrows wheeler transform for terabases"):
  with members:
    - "a massively parallel algorithm for constructing the bwt of large string sets"
    - "from theory to practice plug and play with succinct data structures"
    - "lightweight data indexing and compression in external memory"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-5 (induced by source paper "an external memory algorithm for string graph construction"):
  with members:
    - "covering pairs in directed acyclic graphs"
    - "lightweight lcp construction for next generation sequencing datasets"
    - "lightweight data indexing and compression in external memory"

Connected through hyperedge HE-6 (induced by source paper "disk based genome sequencing data compression"):
  with members:
    - "kmc 2 fast and resource frugal k mer counting"

Connected through hyperedge HE-7 (induced by source paper "a massively parallel algorithm for constructing the bwt of large string sets"):
  with members:
    - "lightweight data indexing and compression in external memory"

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)