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: "burrows wheeler transform for terabases"
  Abstract: In order to avoid the reference bias introduced by mapping reads to a reference genome, bioinformaticians are investigating reference-free methods for analyzing sequenced genomes. With large projects sequencing thousands of individuals, this raises the need for tools capable of handling terabases of sequence data. A key method is the Burrows-Wheeler transform (BWT), which is widely used for compressing and indexing reads. We propose a practical algorithm for building the BWT of a large read collection by merging the BWTs of subcollections. With our 2.4 Tbp datasets, the algorithm can merge 600 Gbp/day on a single system, using 30 gigabytes of memory overhead on top of the run-length encoded BWTs.

Connected through hyperedge HE-1 (induced by source paper "lightweight merging of compressed indices based on bwt variants"):
  with members:
    - "space efficient merging of succinct de bruijn graphs"
    - "external memory bwt and lcp computation for sequence collections with applications"
    - "indexing variation graphs"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "lightweight lcp construction for very large collections of strings"
    - "linear time construction of compressed text indices in compact space"
    - "variable order de bruijn graphs"

Connected through hyperedge HE-2 (induced by source paper "haplotype aware graph indexes"):
  with members:
    - "efficient construction of a complete index for pan genomics read alignment"
    - "indexing variation graphs"
    - "optimal time text indexing in bwt runs bounded space"
    - "from theory to practice plug and play with succinct data structures"

Connected through hyperedge HE-3 (induced by source paper "prefix free parsing for building big bwts"):
  with members:
    - "from lz77 to the run length encoded burrows wheeler transform and back"
    - "optimal time text indexing in bwt runs bounded space"
    - "from theory to practice plug and play with succinct data structures"
    - "lightweight data indexing and compression in external memory"

Connected through hyperedge HE-4 (induced by source paper "scalable construction of text indexes"):
  with members:
    - "thrill high performance algorithmic distributed batch data processing with c"

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)