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: "quasi succinct indices"
  Abstract: Compressed inverted indices in use today are based on the idea of gap compression: documents pointers are stored in increasing order, and the gaps between successive document pointers are stored using suitable codes which represent smaller gaps using less bits. Additional data such as counts and positions is stored using similar techniques. A large body of research has been built in the last 30 years around gap compression, including theoretical modeling of the gap distribution, specialized instantaneous codes suitable for gap encoding, and ad hoc document reorderings which increase the efficiency of instantaneous codes. This paper proposes to represent an index using a different architecture based on quasi-succinct representation of monotone sequences. We show that, besides being theoretically elegant and simple, the new index provides expected constant-time operations and, in practice, significant performance improvements on conjunctive, phrasal and proximity queries.

Connected through hyperedge HE-1 (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"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"

Connected through hyperedge HE-2 (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"
    - "vectorized vbyte decoding"
    - "decoding billions of integers per second through vectorization"

Connected through hyperedge HE-3 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations"):
  with members:
    - "grammar based graph compression"
    - "compressing networks with super nodes"
    - "graphzip dictionary based compression for mining graph streams"
    - "parallel construction of compact planar embeddings"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "fully dynamic de bruijn graphs"
    - "compressing graphs and indexes with recursive graph bisection"

Connected through hyperedge HE-4 (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"
    - "vectorized vbyte decoding"
    - "asymmetric numeral systems entropy coding combining speed of huffman coding with compression rate of arithmetic coding"
    - "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-5 (induced by source paper "handling massive n gram datasets efficiently"):
  with members:
    - "fast small and exact infinite order language modelling with compressed suffix trees"
    - "strategies for training large vocabulary neural language models"
    - "cache oblivious peeling of random hypergraphs"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-6 (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"
    - "vectorized vbyte decoding"
    - "simd compression and the intersection of sorted integers"
    - "decoding billions of integers per second through vectorization"

Connected through hyperedge HE-7 (induced by source paper "off the beaten path let s replace term based retrieval with k nn search"):
  with members:
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "practical and optimal lsh for angular distance"
    - "permutation search methods are efficient yet faster search is possible"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "hashing for similarity search a survey"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-8 (induced by source paper "compact indexes for flexible top k retrieval"):
  with members:
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "from theory to practice plug and play with succinct data structures"
    - "decoding billions of integers per second through vectorization"
    - "faster compact top k document retrieval"
    - "new algorithms on wavelet trees and applications to information retrieval"

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)