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: "calf categorical automata learning framework"
  Abstract: Automata learning is a technique that has successfully been applied in verification, with the automaton type varying depending on the application domain. Adaptations of automata learning algorithms for increasingly complex types of automata have to be developed from scratch because there was no abstract theory offering guidelines. This makes it hard to devise such algorithms, and it obscures their correctness proofs. We introduce a simple category-theoretic formalism that provides an appropriately abstract foundation for studying automata learning. Furthermore, our framework establishes formal relations between algorithms for learning, testing, and minimization. We illustrate its generality with two examples: deterministic and weighted automata.

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"
    - "learning automata with side effects"

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"
    - "learning automata with side effects"
    - "eilenberg theorems for free"
    - "syntactic monoids in a category"

Connected through hyperedge HE-3 (induced by source paper "coalgebra learning via duality"):
  with members:
    - "learning automata with side effects"
    - "coalgebraic trace semantics via forgetful logics"
    - "well pointed coalgebras"

Connected through hyperedge HE-4 (induced by source paper "learning automata with side effects"):
  with members:
    - "coalgebraic determinization of alternating automata"
    - "linear temporal logic for regular cost functions"

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)