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 "better bitmap performance with roaring bitmaps"):
  Abstract: Bitmap indexes are commonly used in databases and search engines. By exploiting bit-level parallelism, they can significantly accelerate queries. However, they can use much memory, and thus we might prefer compressed bitmap indexes. Following Oracle's lead, bitmaps are often compressed using run-length encoding (RLE). Building on prior work, we introduce the Roaring compressed bitmap format: it uses packed arrays for compression instead of RLE. We compare it to two high-performance RLE-based bitmap encoding techniques: WAH (Word Aligned Hybrid compression scheme) and Concise (Compressed `n' Composable Integer Set). On synthetic and real data, we find that Roaring bitmaps (1) often compress significantly better (e.g., 2 times) and (2) are faster than the compressed alternatives (up to 900 times faster for intersections). Our results challenge the view that RLE-based bitmap compression is best.

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 "compressed bitmap indexes beyond unions and intersections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (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-6 (induced by source paper "roaring bitmaps implementation of an optimized software library")
  Member "attribute value reordering for efficient hybrid olap":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reordering columns for smaller indexes")

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)