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: "linear size cdawg new repetition aware indexing and grammar compression"
  Abstract: In this paper, we propose a novel approach to combine \emph{compact directed acyclic word graphs} (CDAWGs) and grammar-based compression. This leads us to an efficient self-index, called Linear-size CDAWGs (L-CDAWGs), which can be represented with $O(\tilde e_T \log n)$ bits of space allowing for $O(\log n)$-time random and $O(1)$-time sequential accesses to edge labels, and $O(m \log \sigma + occ)$-time pattern matching. Here, $\tilde e_T$ is the number of all extensions of maximal repeats in $T$, $n$ and $m$ are respectively the lengths of the text $T$ and a given pattern, $\sigma$ is the alphabet size, and $occ$ is the number of occurrences of the pattern in $T$. The repetitiveness measure $\tilde e_T$ is known to be much smaller than the text length $n$ for highly repetitive text. For constant alphabets, our L-CDAWGs achieve $O(m + occ)$ pattern matching time with $O(e_T^r \log n)$ bits of space, which improves the pattern matching time of Belazzougui et al.'s run-length BWT-CDAWGs by a factor of $\log \log n$, with the same space complexity. Here, $e_T^r$ is the number of right extensions of maximal repeats in $T$. As a byproduct, our result gives a way of constructing an SLP of size $O(\tilde e_T)$ for a given text $T$ in $O(n + \tilde e_T \log \sigma)$ time.

Connected through hyperedge HE-1 (induced by source paper "top tree compression of tries"):
  with members:
    - "dynamic packed compact tries revisited"
    - "universal compressed text indexing"
    - "packed compact tries a fast and efficient data structure for online string processing"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "deterministic indexing for packed strings"
    - "composite repetition aware data structures"
    - "tree compression with top trees revisited"

Connected through hyperedge HE-2 (induced by source paper "suffix trees dawgs and cdawgs for forward and backward tries"):
  with members:
    - "fast label extraction in the cdawg"
    - "alphabet dependent string searching with wexponential search trees"
    - "faster compact on line lempel ziv factorization"
    - "fast computation of subpath kernel for trees"

Connected through hyperedge HE-3 (induced by source paper "online algorithms for constructing linear size suffix trie"):
  with members:
    - "fast label extraction in the cdawg"
    - "optimal dynamic strings"
    - "alphabet dependent string searching with wexponential search trees"
    - "on line construction of position heaps"

Connected through hyperedge HE-4 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "faster repetition aware compressed suffix trees based on block trees"
    - "optimal time dictionary compressed indexes"
    - "fast prefix search in little space with applications"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "prefix free parsing for building big bwts"

Connected through hyperedge HE-5 (induced by source paper "mr repair grammar compression based on maximal repeats"):
  with members:
    - "representing the suffix tree with the cdawg"
    - "fast label extraction in the cdawg"
    - "scalable partial least squares regression on grammar compressed data matrices"
    - "space efficient re pair compression"
    - "composite repetition aware data structures"

Connected through hyperedge HE-6 (induced by source paper "optimal time text indexing in bwt runs bounded space"):
  with members:
    - "fast label extraction in the cdawg"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "fully dynamic data structure for lce queries in compressed space"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "composite repetition aware data structures"
    - "computing lz77 in run compressed space"
    - "queries on lz bounded encodings"

Connected through hyperedge HE-7 (induced by source paper "fast label extraction in the cdawg"):
  with members:
    - "representing the suffix tree with the cdawg"
    - "traversing grammar compressed trees with constant delay"
    - "composite repetition aware data structures"
    - "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)