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: "exploiting syntactic structure for natural language modeling"
  Abstract: The thesis presents an attempt at using the syntactic structure in natural language for improved language models for speech recognition. The structured language model merges techniques in automatic parsing and language modeling using an original probabilistic parameterization of a shift-reduce parser. A maximum likelihood reestimation procedure belonging to the class of expectation-maximization algorithms is employed for training the model. Experiments on the Wall Street Journal, Switchboard and Broadcast News corpora show improvement in both perplexity and word error rate - word lattice rescoring - over the standard 3-gram language model. The significance of the thesis lies in presenting an original approach to language modeling that uses the hierarchical - syntactic - structure in natural language to improve on current 3-gram modeling techniques for large vocabulary speech recognition.

Connected through hyperedge HE-1 (induced by source paper "probabilistic top down parsing and language modeling"):
  with members:
    - "exploiting diversity in natural language processing combining parsers"
    - "recognition performance of a structured language model"
    - "estimators for stochastic unification based grammars"
    - "efficient probabilistic top down and left corner parsing"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "refinement of a structured language model"
    - "expoiting syntactic structure for language modeling"

Connected through hyperedge HE-2 (induced by source paper "robust probabilistic predictive syntactic processing"):
  with members:
    - "probabilistic top down parsing and language modeling"
    - "compact non left recursive grammars using the selective left corner transform and factoring"
    - "recognition performance of a structured language model"
    - "efficient probabilistic top down and left corner parsing"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "refinement of a structured language model"
    - "expoiting syntactic structure for language modeling"

Connected through hyperedge HE-3 (induced by source paper "combining semantic and syntactic structure for language modeling"):
  with members:
    - "parsing with the shortest derivation"

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)