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: "reordering columns for smaller indexes"
  Abstract: Column-oriented indexes-such as projection or bitmap indexes-are compressed by run-length encoding to reduce storage and increase speed. Sorting the tables improves compression. On realistic data sets, permuting the columns in the right order before sorting can reduce the number of runs by a factor of two or more. Unfortunately, determining the best column order is NP-hard. For many cases, we prove that the number of runs in table columns is minimized if we sort columns by increasing cardinality. Experimentally, sorting based on Hilbert space-filling curves is poor at minimizing the number of runs.

Connected through hyperedge HE-1 (induced by source paper "on the complexity of bwt runs minimization via alphabet reordering"):
  with members:
    - "on the hardness and inapproximability of recognizing wheeler graphs"
    - "resolution of the burrows wheeler transform conjecture"
    - "string synchronizing sets sublinear time bwt construction and optimal lce data structure"
    - "tunneling on wheeler graphs"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "optimal construction of compressed indexes for highly repetitive texts"
    - "at the roots of dictionary compression string attractors"
    - "optimal time text indexing in bwt runs bounded space"

Connected through hyperedge HE-2 (induced by source paper "compressed bitmap indexes beyond unions and intersections"):
  with members:
    - "threshold and symmetric functions over bitmaps"
    - "sorting improves word aligned bitmap indexes"
    - "concise compressed n composable integer set"

Connected through hyperedge HE-3 (induced by source paper "threshold and symmetric functions over bitmaps"):
  with members:
    - "sorting improves word aligned bitmap indexes"
    - "concise compressed n composable integer set"

Connected through hyperedge HE-4 (induced by source paper "decoding billions of integers per second through vectorization"):
  with members:
    - "quasi succinct indices"
    - "processing a trillion cells per mouse click"
    - "fast set intersection in memory"

Connected through hyperedge HE-5 (induced by source paper "reordering rows for better compression beyond the lexicographic order"):
  with members:
    - "sorting improves word aligned bitmap indexes"
    - "teaching an old elephant new tricks"
    - "a comparison of five probabilistic view size estimation techniques in olap"

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.DB(Databases)