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: "a canonical form for weighted automata and applications to approximate minimization"
  Abstract: We study the problem of constructing approximations to a weighted automaton. Weighted finite automata (WFA) are closely related to the theory of rational series. A rational series is a function from strings to real numbers that can be computed by a finite WFA. Among others, this includes probability distributions generated by hidden Markov models and probabilistic automata. The relationship between rational series and WFA is analogous to the relationship between regular languages and ordinary automata. Associated with such rational series are infinite matrices called Hankel matrices which play a fundamental role in the theory of minimal WFA. Our contributions are: (1) an effective procedure for computing the singular value decomposition (SVD) of such infinite Hankel matrices based on their representation in terms of finite WFA; (2) a new canonical form for finite WFA based on this SVD decomposition; and, (3) an algorithm to construct approximate minimizations of a given WFA. The goal of our approximate minimization algorithm is to start from a minimal WFA and produce a smaller WFA that is close to the given one in a certain sense. The desired size of the approximating automaton is given as input. We give bounds describing how well the approximation emulates the behavior of the original WFA.

Connected through hyperedge HE-1 (induced by source paper "weighted automata extraction from recurrent neural networks via regression on state spaces"):
  with members:
    - "state regularized recurrent neural networks"
    - "intrusion prediction with system call sequence to sequence model"
    - "explaining black boxes on sequential data using weighted automata"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "connecting weighted automata and recurrent neural networks through spectral learning"
    - "bisimulation metrics for weighted automata"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "crafting adversarial input sequences for recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "singular value automata and approximate minimization"):
  with members:
    - "stability and complexity of minimising probabilistic automata"
    - "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-3 (induced by source paper "on the metric based approximate minimization of markov chains"):
  with members:
    - "on the total variation distance of labelled markov chains"
    - "metrics for finite markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "bisimulation metrics for weighted automata"):
  with members:
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "low rank approximation of weighted tree automata"):
  with members:
    - "minimisation of multiplicity tree automata"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

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)