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: "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.

Connected through hyperedge HE-1 (induced by source paper "techniques for inverted index compression"):
  with members:
    - "on optimally partitioning variable byte codes"
    - "on slicing sorted integer sequences"
    - "stream vbyte faster byte oriented integer compression"
    - "roaring bitmaps implementation of an optimized software library"
    - "compressing graphs and indexes with recursive graph bisection"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "quasi succinct indices"

Connected through hyperedge HE-2 (induced by source paper "on slicing sorted integer sequences"):
  with members:
    - "on optimally partitioning variable byte codes"
    - "stream vbyte faster byte oriented integer compression"
    - "roaring bitmaps implementation of an optimized software library"
    - "a fast x86 implementation of select"
    - "vectorized vbyte decoding"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"

Connected through hyperedge HE-3 (induced by source paper "efficient computation of positional population counts using simd instructions"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "faster population counts using avx2 instructions"
    - "stochastic generative hashing"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-4 (induced by source paper "understanding data science lifecycle provenance via graph segmentation and summarization"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "keystoneml optimizing pipelines for large scale advanced analytics"
    - "context free path queries on rdf graphs"
    - "aggregation by provenance types a technique for summarising provenance graphs"
    - "provabs model policy and tooling for abstracting prov graphs"
    - "openml networked science in machine learning"

Connected through hyperedge HE-5 (induced by source paper "faster population counts using avx2 instructions"):
  with members:
    - "simd compression and the intersection of sorted integers"
    - "better bitmap performance with roaring bitmaps"

Connected through hyperedge HE-6 (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"
    - "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"
    - "sorting improves word aligned bitmap indexes"

Connected through hyperedge HE-7 (induced by source paper "upscaledb efficient integer key compression in a key value store using simd instructions"):
  with members:
    - "a general simd based approach to accelerating compression algorithms"
    - "vectorized vbyte decoding"
    - "simd compression and the intersection of sorted integers"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"

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)