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: "random access to grammar compressed strings"
  Abstract: Grammar based compression, where one replaces a long string by a small context-free grammar that generates the string, is a simple and powerful paradigm that captures many popular compression schemes. In this paper, we present a novel grammar representation that allows efficient random access to any character or substring without decompressing the string. #R##N#Let $S$ be a string of length $N$ compressed into a context-free grammar $\mathcal{S}$ of size $n$. We present two representations of $\mathcal{S}$ achieving $O(\log N)$ random access time, and either $O(n\cdot \alpha_k(n))$ construction time and space on the pointer machine model, or $O(n)$ construction time and space on the RAM. Here, $\alpha_k(n)$ is the inverse of the $k^{th}$ row of Ackermann's function. Our representations also efficiently support decompression of any substring in $S$: we can decompress any substring of length $m$ in the same complexity as a single random access query and additional $O(m)$ time. Combining these results with fast algorithms for uncompressed approximate string matching leads to several efficient algorithms for approximate string matching on grammar-compressed strings without decompression. For instance, we can find all approximate occurrences of a pattern $P$ with at most $k$ errors in time $O(n(\min\{|P|k, k^4 + |P|\} + \log N) + occ)$, where $occ$ is the number of occurrences of $P$ in $S$. Finally, we generalize our results to navigation and other operations on grammar-compressed ordered trees. #R##N#All of the above bounds significantly improve the currently best known results. To achieve these bounds, we introduce several new techniques and data structures of independent interest, including a predecessor data structure, two "biased" weighted ancestor data structures, and a compact representation of heavy paths in grammars.

Connected through hyperedge HE-1 (induced by source paper "local decode and update for big data compression"):
  with members:
    - "how to store a random walk"
    - "set membership with non adaptive bit probes"
    - "compressed data structures for dynamic sequences"
    - "set membership with a few bit probes"
    - "bounds on the parameters of locally recoverable codes"
    - "a family of optimal locally recoverable codes"
    - "update efficiency and local repairability limits for capacity approaching codes"
    - "on locally decodable source coding"

Connected through hyperedge HE-2 (induced by source paper "tree compression with top trees revisited"):
  with members:
    - "from theory to practice plug and play with succinct data structures"
    - "approximation of smallest linear tree grammar"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-3 (induced by source paper "rank select and access in grammar compressed strings"):
  with members:
    - "compressed subsequence matching and packed tree coloring"
    - "compact q gram profiling of compressed strings"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "improved grammar based compressed indexes"
    - "fast q gram mining on slp compressed strings"
    - "unified compression based acceleration of edit distance computation"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"

Connected through hyperedge HE-4 (induced by source paper "searching and indexing genomic databases via kernelization"):
  with members:
    - "fully online grammar compression in constant space"
    - "hybrid indexes for repetitive datasets"
    - "improved esp index a practical self index for highly repetitive texts"
    - "indexes of large genome collections on a pc"
    - "data structure lower bounds on random access to grammar compressed strings"
    - "faster approximate pattern matching in compressed repetitive texts"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-5 (induced by source paper "the complexity of compressed membership problems for finite automata"):
  with members:
    - "recompression a simple and powerful technique for word equations"
    - "tying up the loose ends in fully lzw compressed pattern matching"
    - "pattern matching in lempel ziv compressed strings fast simple and deterministic"
    - "faster fully compressed pattern matching by recompression"

Connected through hyperedge HE-6 (induced by source paper "optimal time random access to grammar compressed strings in small space"):
  with members:
    - "rank select and access in grammar compressed strings"
    - "dynamic integer sets with optimal rank select and predecessor search"
    - "data structure lower bounds on random access to grammar compressed strings"

Connected through hyperedge HE-7 (induced by source paper "compressed subsequence matching and packed tree coloring"):
  with members:
    - "faster subsequence recognition in compressed strings"

Connected through hyperedge HE-8 (induced by source paper "xml compression via dags"):
  with members:
    - "fdb a query engine for factorised relational databases"
    - "fast and tiny structural self indexes for xml"
    - "identifying hierarchical structure in sequences a linear time algorithm"

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)