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 "techniques for inverted index compression"):
  Abstract: The data structure at the core of large-scale search engines is the inverted index, which is essentially a collection of sorted integer sequences called inverted lists. Because of the many documents indexed by such engines and stringent performance requirements imposed by the heavy load of queries, the inverted index stores billions of integers that must be searched efficiently. In this scenario, index compression is essential because it leads to a better exploitation of the computer memory hierarchy for faster query processing and, at the same time, allows reducing the number of storage machines. The aim of this article is twofold: first, surveying the encoding algorithms suitable for inverted index compression and, second, characterizing the performance of the inverted index through experimentation.

Includes members:
  Member "on optimally partitioning variable byte codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on slicing sorted integer sequences")
  Member "on slicing sorted integer sequences":
    also connected through other hyperedges:
      (none)
  Member "stream vbyte faster byte oriented integer compression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on slicing sorted integer sequences")
  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 "compressing graphs and indexes with recursive graph bisection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent")
  Member "consistently faster and smaller compressed bitmaps with roaring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on slicing sorted integer sequences")
  Member "vectorized vbyte decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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)