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: "assembling omnitigs using hidden order de bruijn graphs"
  Abstract: De novo DNA assembly is a fundamental task in Bioinformatics, and finding Eulerian paths on de Bruijn graphs is one of the dominant approaches to it. In most of the cases, there may be no one order for the de Bruijn graph that works well for assembling all of the reads. For this reason, some de Bruijn-based assemblers try assembling on several graphs of increasing order, in turn. Boucher et al. (2015) went further and gave a representation making it possible to navigate in the graph and change order on the fly, up to a maximum $K$, but they can use up to $\lg K$ extra bits per edge because they use an LCP array. In this paper, we replace the LCP array by a succinct representation of that array's Cartesian tree, which takes only 2 extra bits per edge and still lets us support interesting navigation operations efficiently. These operations are not enough to let us easily extract unitigs and only unitigs from the graph but they do let us extract a set of safe strings that contains all unitigs. Suppose we are navigating in a variable-order de Bruijn graph representation, following these rules: if there are no outgoing edges then we reduce the order, hoping one appears; if there is exactly one outgoing edge then we take it (increasing the current order, up to $K$); if there are two or more outgoing edges then we stop. Then we traverse a (variable-order) path such that we cross edges only when we have no choice or, equivalently, we generate a string appending characters only when we have no choice. It follows that the strings we extract are safe. Our experiments show we extract a set of strings more informative than the unitigs, while using a reasonable amount of memory.

Connected through hyperedge HE-1 (induced by source paper "fully functional bidirectional burrows wheeler indexes"):
  with members:
    - "simulating the dna string graph in succinct space"
    - "representing the suffix tree with the cdawg"
    - "fast label extraction in the cdawg"
    - "linear time string indexing and analysis in small space"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "linear time construction of compressed text indices in compact space"
    - "variable order de bruijn graphs"
    - "engineering small space dictionary matching"

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)