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: "boosting applied to word sense disambiguation"
  Abstract: In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on supervised WSD. In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating the algorithm by reducing the feature space are studied. The best variant, which we call LazyBoosting, is tested on the largest sense-tagged corpus available containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmark algorithms.

Connected through hyperedge HE-1 (induced by source paper "no spare parts sharing part detectors for image categorization"):
  with members:
    - "object localization in imagenet by looking out of the window"
    - "mid level deep pattern mining"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "spotlight the negatives a generalized discriminative latent model"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "combining knowledge and corpus based word sense disambiguation methods"):
  with members:
    - "assessing system agreement and instance difficulty in the lexical sample tasks of senseval 2"
    - "decision lists for english and basque"
    - "knowledge sources for word sense disambiguation"
    - "the role of conceptual relations in word sense disambiguation"

Connected through hyperedge HE-3 (induced by source paper "machine learning in automated text categorization"):
  with members:
    - "an experimental comparison of naive bayesian and keyword based anti spam filtering with personal e mail messages"
    - "learning to resolve natural language ambiguities a unified approach"

Connected through hyperedge HE-4 (induced by source paper "a comparison between supervised learning algorithms for word sense disambiguation"):
  with members:
    - "naive bayes and exemplar based approaches to word sense disambiguation revisited"
    - "exploring automatic word sense disambiguation with decision lists and the web"
    - "a winnow based approach to context sensitive spelling correction"
    - "learning to resolve natural language ambiguities a unified approach"
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-5 (induced by source paper "exploring automatic word sense disambiguation with decision lists and the web"):
  with members:
    - "naive bayes and exemplar based approaches to word sense disambiguation revisited"
    - "enriching very large ontologies using the www"

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)