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: "learning automata with side effects"
  Abstract: Automata learning has been successfully applied in the verification of hardware and software. The size of the automaton model learned is a bottleneck for scalability and hence optimizations that enable learning of compact representations are important. In this paper, we continue the development of a general framework for automata learning based on category theory and develop a class of optimizations and an accompanying correctness proof for learning algorithms. The new algorithm is parametric on a monad, which provides a rich algebraic structure to capture non-determinism and other side-effects. These side-effects are used to learn more compact automaton models and the abstract categorical approach enables us to capture several possible optimizations under the same (p)roof.

Connected through hyperedge HE-1 (induced by source paper "a categorical framework for learning generalised tree automata"):
  with members:
    - "learning weighted automata over principal ideal domains"
    - "coalgebra learning via duality"
    - "automata learning an algebraic approach"
    - "learning nominal automata"
    - "calf categorical automata learning framework"

Connected through hyperedge HE-2 (induced by source paper "automata learning an algebraic approach"):
  with members:
    - "tree automata as algebras minimisation and determinisation"
    - "coalgebra learning via duality"
    - "generic partition refinement and weighted tree automata"
    - "efficient coalgebraic partition refinement"
    - "learning nominal automata"
    - "calf categorical automata learning framework"
    - "eilenberg theorems for free"
    - "syntactic monoids in a category"

Connected through hyperedge HE-3 (induced by source paper "learning weighted automata over principal ideal domains"):
  with members:
    - "optimizing automata learning via monads"
    - "learning nominal automata"
    - "weighted automata in text and speech processing"

Connected through hyperedge HE-4 (induced by source paper "coalgebra learning via duality"):
  with members:
    - "calf categorical automata learning framework"
    - "coalgebraic trace semantics via forgetful logics"
    - "well pointed coalgebras"

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)