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 "variable byte encoding is now space efficient too"):
  Abstract: The ubiquitous Variable-Byte encoding is considered one of the fastest compressed representation for integer sequences. However, its compression ratio is usually not competitive with other more sophisticated encoders, especially when the integers to be compressed are small that is the typical case for inverted indexes. This paper shows that the compression ratio of Variable-Byte can be improved by $2\times$ by adopting a partitioned representation of the inverted lists. This makes Variable-Byte surprisingly competitive in space with the best bit-aligned encoders, hence disproving the folklore belief that Variable-Byte is space-inefficient for inverted index compression. Despite the significant space savings, we show that our optimization almost comes for free, given that: we introduce an optimal partitioning algorithm that, by running in linear time and with low constant factors, does not affect indexing time; we show that the query processing speed of Variable-Byte is preserved, with an extensive experimental analysis and comparison with several other state-of-the-art encoders.

Includes members:
  Member "stream vbyte faster byte oriented integer compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "techniques for inverted index compression")
  Member "universal indexes for highly repetitive document collections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rpair rescaling repair with rsync")
  Member "vectorized vbyte decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on optimally partitioning variable byte codes")
  Member "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on optimally partitioning variable byte codes")
  Member "quasi succinct indices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "permute to train a new dimension to training deep neural networks")
  Member "on optimally partitioning a text to improve its compression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on optimally partitioning variable byte codes")
  Member "improving table compression with combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on optimally partitioning variable byte codes")

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)