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 diversity in natural language processing combining parsers"
  Abstract: Three state-of-the-art statistical parsers are combined to produce more accurate parses, as well as new bounds on achievable Treebank parsing accuracy. Two general approaches are presented and two combination techniques are described for each approach. Both parametric and non-parametric models are explored. The resulting parsers surpass the best previously published performance results for the Penn Treebank.

Connected through hyperedge HE-1 (induced by source paper "improving semantic parsing for task oriented dialog"):
  with members:
    - "deep contextualized word representations"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "semantic parsing for task oriented dialog using hierarchical representations"
    - "learning an executable neural semantic parser"
    - "sling a framework for frame semantic parsing"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"
    - "learning structured natural language representations for semantic parsing"

Connected through hyperedge HE-2 (induced by source paper "supervised and unsupervised ensembling for knowledge base population"):
  with members:
    - "a simple approach to building ensembles of naive bayesian classifiers for word sense disambiguation"

Connected through hyperedge HE-3 (induced by source paper "diversity in spectral learning for natural language parsing"):
  with members:
    - "settling the polynomial learnability of mixtures of gaussians"
    - "a spectral algorithm for learning hidden markov models"
    - "exploiting diversity for natural language parsing"

Connected through hyperedge HE-4 (induced by source paper "a study on cost behaviors of binary classification measures in class imbalanced problems"):
  with members:
    - "a new strategy of cost free learning in the class imbalance problem"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-5 (induced by source paper "framework and resources for natural language parser evaluation"):
  with members:
    - "a common xml based framework for syntactic annotations"
    - "joint and conditional estimation of tagging and parsing models"
    - "atlas a flexible and extensible architecture for linguistic annotation"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "corpus annotation for parser evaluation"

Connected through hyperedge HE-6 (induced by source paper "probabilistic top down parsing and language modeling"):
  with members:
    - "exploiting syntactic structure for natural language modeling"
    - "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-7 (induced by source paper "using existing systems to supplement small amounts of annotated grammatical relations training data"):
  with members:
    - "corpus annotation for parser evaluation"
    - "learning transformation rules to find grammatical relations"
    - "cascaded grammatical relation assignment"
    - "supervised grammar induction using training data with limited constituent information"

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)