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: "stability and complexity of minimising probabilistic automata"
  Abstract: We consider the state-minimisation problem for weighted and probabilistic automata. We provide a numerically stable polynomial-time minimisation algorithm for weighted automata, with guaranteed bounds on the numerical error when run with floating-point arithmetic. Our algorithm can also be used for "lossy" minimisation with bounded error. We show an application in image compression. In the second part of the paper we study the complexity of the minimisation problem for probabilistic automata. We prove that the problem is NP-hard and in PSPACE, improving a recent EXPTIME-result.

Connected through hyperedge HE-1 (induced by source paper "singular value automata and approximate minimization"):
  with members:
    - "a canonical form for weighted automata and applications to approximate minimization"
    - "reduced rank hidden markov models"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "trace refinement in labelled markov decision processes"):
  with members:
    - "probabilistic bisimulation naturally on distributions"
    - "reachability in two clock timed automata is pspace complete"
    - "when equivalence and bisimulation join forces in probabilistic automata"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "a canonical form for weighted automata and applications to approximate minimization"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "reduced rank hidden markov models"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "minimisation of multiplicity tree automata"):
  with members:
    - "complexity of equivalence and learning for multiplicity tree automata"
    - "on the complexity of equivalence and minimisation for q weighted automata"
    - "residual finite tree automata"
    - "weighted automata in text and speech processing"

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)