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: "sorting improves word aligned bitmap indexes"
  Abstract: Bitmap indexes must be compressed to reduce input/output costs and minimize CPU usage. To accelerate logical operations (AND, OR, XOR) over bitmaps, we use techniques based on run-length encoding (RLE), such as Word-Aligned Hybrid (WAH) compression. These techniques are sensitive to the order of the rows: a simple lexicographical sort can divide the index size by 9 and make indexes several times faster. We investigate row-reordering heuristics. Simply permuting the columns of the table can increase the sorting efficiency by 40%. Secondary contributions include efficient algorithms to construct and aggregate bitmaps. The effect of word length is also reviewed by constructing 16-bit, 32-bit and 64-bit indexes. Using 64-bit CPUs, we find that 64-bit indexes are slightly faster than 32-bit indexes despite being nearly twice as large.

Connected through hyperedge HE-1 (induced by source paper "roaring bitmaps implementation of an optimized software library"):
  with members:
    - "faster population counts using avx2 instructions"
    - "effortless data exploration with zenvisage an expressive and interactive visual analytics system"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "array layouts for comparison based searching"
    - "simd compression and the intersection of sorted integers"
    - "better bitmap performance with roaring bitmaps"
    - "compressed bitmap indexes beyond unions and intersections"
    - "reordering rows for better compression beyond the lexicographic order"

Connected through hyperedge HE-2 (induced by source paper "consistently faster and smaller compressed bitmaps with roaring"):
  with members:
    - "simd compression and the intersection of sorted integers"
    - "better bitmap performance with roaring bitmaps"
    - "compressed bitmap indexes beyond unions and intersections"
    - "decoding billions of integers per second through vectorization"
    - "reordering rows for better compression beyond the lexicographic order"
    - "concise compressed n composable integer set"

Connected through hyperedge HE-3 (induced by source paper "fast in memory sql analytics on graphs"):
  with members:
    - "fast in memory sql analytics on relationships between entities"
    - "join processing for graph patterns an old dog with new tricks"
    - "high throughput transaction executions on graphics processors"

Connected through hyperedge HE-4 (induced by source paper "fast in memory sql analytics on relationships between entities"):
  with members:
    - "high throughput transaction executions on graphics processors"

Connected through hyperedge HE-5 (induced by source paper "speedy browsing and sampling with needletail"):
  with members:
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-6 (induced by source paper "better bitmap performance with roaring bitmaps"):
  with members:
    - "simd compression and the intersection of sorted integers"
    - "compressed bitmap indexes beyond unions and intersections"
    - "decoding billions of integers per second through vectorization"
    - "reordering rows for better compression beyond the lexicographic order"
    - "concise compressed n composable integer set"
    - "attribute value reordering for efficient hybrid olap"

Connected through hyperedge HE-7 (induced by source paper "compressed bitmap indexes beyond unions and intersections"):
  with members:
    - "threshold and symmetric functions over bitmaps"
    - "reordering columns for smaller indexes"
    - "concise compressed n composable integer set"

Connected through hyperedge HE-8 (induced by source paper "threshold and symmetric functions over bitmaps"):
  with members:
    - "reordering columns for smaller indexes"
    - "concise compressed n composable integer set"

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)