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: "shortest unique substring query revisited"
  Abstract: We revisit the problem of finding shortest unique substring (SUS) proposed recently by [6]. We propose an optimal $O(n)$ time and space algorithm that can find an SUS for every location of a string of size $n$. Our algorithm significantly improves the $O(n^2)$ time complexity needed by [6]. We also support finding all the SUSes covering every location, whereas the solution in [6] can find only one SUS for every location. Further, our solution is simpler and easier to implement and can also be more space efficient in practice, since we only use the inverse suffix array and longest common prefix array of the string, while the algorithm in [6] uses the suffix tree of the string and other auxiliary data structures. Our theoretical results are validated by an empirical study that shows our algorithm is much faster and more space-saving than the one in [6].

Connected through hyperedge HE-1 (induced by source paper "shortest unique palindromic substring queries on run length encoded strings"):
  with members:
    - "eertree an efficient data structure for processing palindromes in strings"
    - "an in place framework for exact and approximate shortest unique substring queries"

Connected through hyperedge HE-2 (induced by source paper "shortest unique palindromic substring queries in optimal time"):
  with members:
    - "diverse palindromic factorization is np complete"
    - "eertree an efficient data structure for processing palindromes in strings"
    - "an in place framework for exact and approximate shortest unique substring queries"
    - "a subquadratic algorithm for minimum palindromic factorization"

Connected through hyperedge HE-3 (induced by source paper "tight bound on the maximum number of shortest unique substrings"):
  with members:
    - "on stabbing queries for generalized longest repeat"
    - "an in place framework for exact and approximate shortest unique substring queries"

Connected through hyperedge HE-4 (induced by source paper "a framework for space efficient string kernels"):
  with members:
    - "space efficient detection of unusual words"
    - "linear time construction of compressed text indices in compact space"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-5 (induced by source paper "on stabbing queries for generalized longest repeat"):
  with members:
    - "on longest repeat queries"
    - "on longest repeat queries using gpu"

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)