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 "on slicing sorted integer sequences"):
  Abstract: Representing sorted integer sequences in small space is a central problem for large-scale retrieval systems such as Web search engines. Efficient query resolution, e.g., intersection or random access, is achieved by carefully partitioning the sequences. In this work we describe and compare two different partitioning paradigms: partitioning by cardinality and partitioning by universe. Although the ideas behind such paradigms have been known in the coding and algorithmic community since many years, inverted index compression has extensively adopted the former paradigm, whereas the latter has received only little attention. As a result, an experimental comparison between these two is missing for the setting of inverted index compression. We also propose and implement a solution that recursively slices the universe of representation of a sequence to achieve compact storage and attain to fast query execution. Albeit larger than some state-of-the-art representations, this slicing approach substantially improves the performance of list intersections and unions while operating in compressed space, thus offering an excellent space/time trade-off for the problem.

Includes members:
  Member "on optimally partitioning variable byte codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "techniques for inverted index compression")
  Member "stream vbyte faster byte oriented integer compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "techniques for inverted index compression")
  Member "roaring bitmaps implementation of an optimized software library":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cartolabe a web based scalable visualization of large document collections")
  Member "a fast x86 implementation of select":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rank and select another lesson learned")
  Member "consistently faster and smaller compressed bitmaps with roaring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "techniques for inverted index compression")
  Member "vectorized vbyte decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on optimally partitioning variable byte codes")
  Member "quasi succinct indices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "techniques for inverted index compression")
  Member "decoding billions of integers per second through vectorization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "permute to train a new dimension to training deep neural networks")

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.IR(Information Retrieval)