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: "weighted automata in text and speech processing"
  Abstract: Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consider more general machines in which arcs are assigned weights or costs. We briefly describe some of the main theoretical and algorithmic aspects of these machines. In particular, we describe an efficient composition algorithm for weighted transducers, and give examples illustrating the value of determinization and minimization algorithms for weighted automata.

Connected through hyperedge HE-1 (induced by source paper "learning weighted automata over principal ideal domains"):
  with members:
    - "optimizing automata learning via monads"
    - "learning nominal automata"
    - "learning automata with side effects"

Connected through hyperedge HE-2 (induced by source paper "online non additive path learning under full and partial information"):
  with members:
    - "the on line shortest path problem under partial monitoring"

Connected through hyperedge HE-3 (induced by source paper "sequence prediction with neural segmental models"):
  with members:
    - "optimizing expected word error rate via sampling for speech recognition"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"
    - "wav2letter an end to end convnet based speech recognition system"
    - "an embedded segmental k means model for unsupervised segmentation and clustering of speech"
    - "multitask learning with ctc and segmental crf for speech recognition"
    - "end to end training approaches for discriminative segmental models"
    - "segmental recurrent neural networks for end to end speech recognition"

Connected through hyperedge HE-4 (induced by source paper "generalization bounds for weighted automata"):
  with members:
    - "pre reduction graph products hardnesses of properly learning dfas and approximating edp on dags"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-5 (induced by source paper "minimisation of multiplicity tree automata"):
  with members:
    - "stability and complexity of minimising probabilistic automata"
    - "complexity of equivalence and learning for multiplicity tree automata"
    - "on the complexity of equivalence and minimisation for q weighted automata"
    - "residual finite tree automata"

Connected through hyperedge HE-6 (induced by source paper "linear space computation of the edit distance between a string and a finite automaton"):
  with members:
    - "3 way composition of weighted finite state transducers"

Connected through hyperedge HE-7 (induced by source paper "general algorithms for testing the ambiguity of finite automata"):
  with members:
    - "3 way composition of weighted finite state transducers"

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.CL(Computation and Language)