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: "coalgebra learning via duality"
  Abstract: Automata learning is a popular technique for inferring minimal automata through membership and equivalence queries. In this paper, we generalise learning to the theory of coalgebras. The approach relies on the use of logical formulas as tests, based on a dual adjunction between states and logical theories. This allows us to learn, e.g., labelled transition systems, using Hennessy-Milner logic. Our main contribution is an abstract learning algorithm, together with a proof of correctness and termination.

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"
    - "automata learning an algebraic approach"
    - "learning nominal automata"
    - "calf categorical automata learning framework"
    - "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"
    - "generic partition refinement and weighted tree automata"
    - "efficient coalgebraic partition refinement"
    - "learning nominal automata"
    - "calf categorical automata learning framework"
    - "learning automata with side effects"
    - "eilenberg theorems for free"
    - "syntactic monoids in a category"

Connected through hyperedge HE-3 (induced by source paper "tree automata as algebras minimisation and determinisation"):
  with members:
    - "efficient coalgebraic partition refinement"
    - "coalgebraic trace semantics via forgetful logics"
    - "generic trace semantics via coinduction"

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.LO(Logic in Computer Science)