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: "practical experiments with regular approximation of context free languages"
  Abstract: Several methods are discussed that construct a finite automaton given a context-free grammar, including both methods that lead to subsets and those that lead to supersets of the original context-free language. Some of these methods of regular approximation are new, and some others are presented here in a more refined form with respect to existing literature. Practical experiments with the different methods of regular approximation are performed for spoken-language input: hypotheses from a speech recognizer are filtered through a finite automaton.

Connected through hyperedge HE-1 (induced by source paper "grammatical sequence prediction for real time neural semantic parsing"):
  with members:
    - "symbolic priors for rnn based semantic parsing"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "vocabulary selection strategies for neural machine translation"
    - "a syntactic neural model for general purpose code generation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "language to logical form with neural attention"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"

Connected through hyperedge HE-2 (induced by source paper "online learning with automata based expert sequences"):
  with members:
    - "tracking the best expert in non stationary stochastic environments"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "online optimization competing with dynamic comparators"
    - "strongly adaptive online learning"
    - "renyi divergence and kullback leibler divergence"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "approximation of weighted automata with storage"):
  with members:
    - "an automata characterisation for multiple context free languages"
    - "context free grammars with storage"

Connected through hyperedge HE-4 (induced by source paper "probabilistic modelling of morphologically rich languages"):
  with members:
    - "compositional morphology for word representations and language modelling"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "efficient estimation of word representations in vector space"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "on the difficulty of training recurrent neural networks"
    - "from frequency to meaning vector space models of semantics"
    - "empirical methods for compound splitting"
    - "unsupervised discovery of morphologically related words based on orthographic and semantic similarity"

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)