human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "consistently faster and smaller compressed bitmaps with roaring"):
  Abstract: Compressed bitmap indexes are used in databases and search engines. Many bitmap compression techniques have been proposed, almost all relying primarily on run-length encoding (RLE). However, on unsorted data, we can get superior performance with a hybrid compression technique that uses both uncompressed bitmaps and packed arrays inside a two-level tree. An instance of this technique, Roaring, has recently been proposed. Due to its good performance, it has been adopted by several production platforms (e.g., Apache Lucene, Apache Spark, Apache Kylin and Druid). #R##N#Yet there are cases where run-length encoded bitmaps are smaller than the original Roaring bitmaps---typically when the data is sorted so that the bitmaps contain long compressible runs. To better handle these cases, we build a new Roaring hybrid that combines uncompressed bitmaps, packed arrays and RLE compressed segments. The result is a new Roaring format that compresses better. #R##N#Overall, our new implementation of Roaring can be several times faster (up to two orders of magnitude) than the implementations of traditional RLE-based alternatives (WAH, Concise, EWAH) while compressing better. We review the design choices and optimizations that make these good results possible.

Includes members:
  Member "simd compression and the intersection of sorted integers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "permute to train a new dimension to training deep neural networks")
  Member "better bitmap performance with roaring bitmaps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a computational model for analytic column stores")
  Member "compressed bitmap indexes beyond unions and intersections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "roaring bitmaps implementation of an optimized software library")
  Member "decoding billions of integers per second through vectorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "permute to train a new dimension to training deep neural networks")
  Member "reordering rows for better compression beyond the lexicographic order":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "roaring bitmaps implementation of an optimized software library")
  Member "sorting improves word aligned bitmap indexes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "roaring bitmaps implementation of an optimized software library")
  Member "concise compressed n composable integer set":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "roaring bitmaps implementation of an optimized software library")

Question: Please tell me which class the source paper 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)