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: "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.

Connected through hyperedge HE-1 (induced by source paper "a computational model for analytic column stores"):
  with members:
    - "the vertica analytic database c store 7 years later"
    - "processing a trillion cells per mouse click"

Connected through hyperedge HE-2 (induced by source paper "faster population counts using avx2 instructions"):
  with members:
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "simd compression and the intersection of sorted integers"

Connected through hyperedge HE-3 (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"
    - "compressed bitmap indexes beyond unions and intersections"
    - "reordering rows for better compression beyond the lexicographic order"
    - "sorting improves word aligned bitmap indexes"

Connected through hyperedge HE-4 (induced by source paper "consistently faster and smaller compressed bitmaps with roaring"):
  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"
    - "sorting improves word aligned bitmap indexes"
    - "concise compressed n composable integer set"

Connected through hyperedge HE-5 (induced by source paper "effortless data exploration with zenvisage an expressive and interactive visual analytics system"):
  with members:
    - "efficient and extensible algorithms for multi query optimization"

Connected through hyperedge HE-6 (induced by source paper "scalable eventually consistent counters over unreliable networks"):
  with members:
    - "consensus on transaction commit"

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)