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: "vectorized vbyte decoding"
  Abstract: We consider the ubiquitous technique of VByte compression, which represents each integer as a variable length sequence of bytes. The low 7 bits of each byte encode a portion of the integer, and the high bit of each byte is reserved as a continuation flag.  This flag is set to 1 for all bytes except the last, and the decoding of each integer is complete when a byte with a high bit of 0 is encountered.  VByte decoding can be a performance bottleneck especially when the unpredictable lengths of the encoded integers cause frequent branch mispredictions.  Previous attempts to accelerate VByte decoding using SIMD vector instructions have been disappointing, prodding search engines such as Google to use more complicated but faster-to-decode formats for performance-critical code. Our decoder (Masked VByte) is 2 to 4 times faster than a conventional scalar VByte decoder, making the format once again competitive with regard to speed.

Connected through hyperedge HE-1 (induced by source paper "on optimally partitioning variable byte codes"):
  with members:
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "on optimally partitioning a text to improve its compression"
    - "improving table compression with combinatorial optimization"

Connected through hyperedge HE-2 (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"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "quasi succinct indices"

Connected through hyperedge HE-3 (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"
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"

Connected through hyperedge HE-4 (induced by source paper "compressed indexes for fast search of semantic data"):
  with members:
    - "techniques for inverted index compression"
    - "handling massive n gram datasets efficiently"
    - "a survey of rdf data management systems"

Connected through hyperedge HE-5 (induced by source paper "variable byte encoding is now space efficient too"):
  with members:
    - "stream vbyte faster byte oriented integer compression"
    - "universal indexes for highly repetitive document collections"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "quasi succinct indices"
    - "decoding billions of integers per second through vectorization"
    - "on optimally partitioning a text to improve its compression"
    - "improving table compression with combinatorial optimization"

Connected through hyperedge HE-6 (induced by source paper "stream vbyte faster byte oriented integer compression"):
  with members:
    - "simd compression and the intersection of sorted integers"

Connected through hyperedge HE-7 (induced by source paper "upscaledb efficient integer key compression in a key value store using simd instructions"):
  with members:
    - "consistently faster and smaller compressed bitmaps with roaring"
    - "a general simd based approach to accelerating compression algorithms"
    - "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.IR(Information Retrieval)