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: "online self indexed grammar compression"
  Abstract: Although several grammar-based self-indexes have been proposed thus far, their applicability is limited to offline settings where whole input texts are prepared, thus requiring to rebuild index structures for given additional inputs, which is often the case in the big data era. In this paper, we present the first online self-indexed grammar compression named OESP-index that can gradually build the index structure by reading input characters one-by-one. Such a property is another advantage which enables saving a working space for construction, because we do not need to store input texts in memory. We experimentally test OESP-index on the ability to build index structures and search query texts, and we show OESP-index's efficiency, especially space-efficiency for building index structures.

Connected through hyperedge HE-1 (induced by source paper "siedm an efficient string index and search algorithm for edit distance with moves"):
  with members:
    - "fully online grammar compression in constant space"
    - "online pattern matching for string edit distance with moves"
    - "searching and indexing genomic databases via kernelization"
    - "improved esp index a practical self index for highly repetitive texts"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-2 (induced by source paper "dynamic index and lz factorization in compressed space"):
  with members:
    - "fully dynamic data structure for lce queries in compressed space"
    - "dynamic data structures for document collections and graphs"
    - "improved esp index a practical self index for highly repetitive texts"
    - "restructuring compressed texts without explicit decompression"

Connected through hyperedge HE-3 (induced by source paper "dynamic index lz factorization and lce queries in compressed space"):
  with members:
    - "approximating lz77 via small space multiple pattern matching"
    - "dynamic data structures for document collections and graphs"
    - "finger search random access and longest common extensions in grammar compressed strings"
    - "fingerprints in compressed strings"
    - "restructuring compressed texts without explicit decompression"

Connected through hyperedge HE-4 (induced by source paper "computing lz77 in run compressed space"):
  with members:
    - "queries on lz bounded encodings"
    - "self index based on lz77 thesis"
    - "large alphabets and incompressibility"

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.DS(Data Structures and Algorithms)