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: "kmc 2 fast and resource frugal k mer counting"
  Abstract: Motivation: Building the histogram of occurrences of every $k$-symbol long substring of nucleotide data is a standard step in many bioinformatics applications, known under the name of $k$-mer counting. Its applications include developing de Bruijn graph genome assemblers, fast multiple sequence alignment and repeat detection. The tremendous amounts of NGS data require fast algorithms for $k$-mer counting, preferably using moderate amounts of memory. #R##N#Results: We present a novel method for $k$-mer counting, on large datasets at least twice faster than the strongest competitors (Jellyfish~2, KMC~1), using about 12\,GB (or less) of RAM memory. Our disk-based method bears some resemblance to MSPKmerCounter, yet replacing the original minimizers with signatures (a carefully selected subset of all minimizers) and using $(k, x)$-mers allows to significantly reduce the I/O, and a highly parallel overall architecture allows to achieve unprecedented processing speeds. For example, KMC~2 allows to count the 28-mers of a human reads collection with 44-fold coverage (106\,GB of compressed size) in about 20 minutes, on a 6-core Intel i7 PC with an SSD. #R##N#Availability: KMC~2 is freely available at this http URL#R##N#Contact: sebastian.deorowicz@polsl.pl

Connected through hyperedge HE-1 (induced by source paper "efficient construction of a complete index for pan genomics read alignment"):
  with members:
    - "prefix free parsing for building big bwts"
    - "optimal time text indexing in bwt runs bounded space"
    - "searching and indexing genomic databases via kernelization"
    - "hybrid indexes for repetitive datasets"
    - "indexes of large genome collections on a pc"

Connected through hyperedge HE-2 (induced by source paper "parallel approach to sliding window sums"):
  with members:
    - "faster and more accurate sequence alignment with snap"

Connected through hyperedge HE-3 (induced by source paper "even faster sorting of not only integers"):
  with members:
    - "blockquicksort how branch mispredictions don t affect quicksort"
    - "sorting data on ultra large scale with raduls new incarnation of radix sort"
    - "new bounds on optimal sorting networks"

Connected through hyperedge HE-4 (induced by source paper "twopaco an efficient algorithm to build the compacted de bruijn graph from many complete genomes"):
  with members:
    - "read mapping on de bruijn graphs"
    - "a representation of a compressed de bruijn graph for pan genome analysis that enables search"
    - "using cascading bloom filters to improve the memory usage for de brujin graphs"
    - "recursive n gram hashing is pairwise independent at best"

Connected through hyperedge HE-5 (induced by source paper "a bloated fm index reducing the number of cache misses during the search"):
  with members:
    - "fm index for dummies"
    - "a practical index for approximate dictionary matching with few mismatches"

Connected through hyperedge HE-6 (induced by source paper "sampling the suffix array with minimizers"):
  with members:
    - "approximating lz77 via small space multiple pattern matching"
    - "two simple full text indexes based on the suffix array"
    - "compressed text indexes from theory to practice"

Connected through hyperedge HE-7 (induced by source paper "disk based genome sequencing data compression"):
  with members:
    - "large scale compression of genomic sequence databases with the burrows wheeler transform"

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)