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: "randomized sliding window algorithms for regular languages"
  Abstract: A sliding window algorithm receives a stream of symbols and has to output at each time instant a certain value which only depends on the last $n$ symbols. If the algorithm is randomized, then at each time instant it produces an incorrect output with probability at most $\epsilon$, which is a constant error bound. This work proposes a more relaxed definition of correctness which is parameterized by the error bound $\epsilon$ and the failure ratio $\phi$: A randomized sliding window algorithm is required to err with probability at most $\epsilon$ at a portion of $1-\phi$ of all time instants of an input stream. This work continues the investigation of sliding window algorithms for regular languages. In previous works a trichotomy theorem was shown for deterministic algorithms: the optimal space complexity is either constant, logarithmic or linear in the window size. The main results of this paper concerns three natural settings (randomized algorithms with failure ratio zero and randomized/deterministic algorithms with bounded failure ratio) and provide natural language theoretic characterizations of the space complexity classes.

Connected through hyperedge HE-1 (induced by source paper "sliding window property testing for regular languages"):
  with members:
    - "visibly pushdown languages over sliding windows"
    - "automata theory on sliding windows"
    - "streaming pattern matching with d wildcards"
    - "approximate hamming distance in a stream"
    - "dictionary matching in a stream"
    - "streaming property testing of visibly pushdown languages"
    - "the k mismatch problem revisited"
    - "streaming algorithms for recognizing nearly well parenthesized expressions"

Connected through hyperedge HE-2 (induced by source paper "streamability of nested word transductions"):
  with members:
    - "untwisting two way transducers in elementary time"
    - "two way visibly pushdown automata and transducers"
    - "streaming property testing of visibly pushdown languages"
    - "from two way to one way finite state transducers"
    - "streaming tree transducers"
    - "on functionality of visibly pushdown transducers"
    - "validating xml documents in the streaming model with external memory"
    - "tight lower bounds for query processing on streaming and external memory data"

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.FL(Formal Languages and Automata Theory)