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: "concise compressed n composable integer set"
  Abstract: Bit arrays, or bitmaps, are used to significantly speed up set operations in several areas, such as data warehousing, information retrieval, and data mining, to cite a few. However, bitmaps usually use a large storage space, thus requiring compression. Nevertheless, there is a space-time tradeoff among compression schemes. The Word Aligned Hybrid (WAH) bitmap compression trades some space to allow for bitwise operations without first decompressing bitmaps. WAH has been recognized as the most efficient scheme in terms of computation time. In this paper we present CONCISE (Compressed 'n' Composable Integer Set), a new scheme that enjoys significatively better performances than those of WAH. In particular, when compared to WAH, our algorithm is able to reduce the required memory up to 50%, by having similar or better performance in terms of computation time. Further, we show that CONCISE can be efficiently used to manipulate bitmaps representing sets of integral numbers in lieu of well-known data structures such as arrays, lists, hashtables, and self-balancing binary search trees. Extensive experiments over synthetic data show the effectiveness of our approach.

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"
    - "sorting improves word aligned bitmap indexes"

Connected through hyperedge HE-3 (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"
    - "sorting improves word aligned bitmap indexes"
    - "attribute value reordering for efficient hybrid olap"

Connected through hyperedge HE-4 (induced by source paper "compressed bitmap indexes beyond unions and intersections"):
  with members:
    - "threshold and symmetric functions over bitmaps"
    - "reordering columns for smaller indexes"
    - "sorting improves word aligned bitmap indexes"

Connected through hyperedge HE-5 (induced by source paper "threshold and symmetric functions over bitmaps"):
  with members:
    - "reordering columns for smaller indexes"
    - "sorting improves word aligned bitmap indexes"

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.DS(Data Structures and Algorithms)