human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a comparison between supervised learning algorithms for word sense disambiguation"):
  Abstract: This paper describes a set of comparative experiments, including cross-corpus evaluation, between five alternative algorithms for supervised Word Sense Disambiguation (WSD), namely Naive Bayes, Exemplar-based learning, SNoW, Decision Lists, and Boosting. Two main conclusions can be drawn: 1) The LazyBoosting algorithm outperforms the other four state-of-the-art algorithms in terms of accuracy and ability to tune to new domains; 2) The domain dependence of WSD systems seems very strong and suggests that some kind of adaptation or tuning is required for cross-corpus application.

Includes members:
  Member "naive bayes and exemplar based approaches to word sense disambiguation revisited":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring automatic word sense disambiguation with decision lists and the web")
  Member "exploring automatic word sense disambiguation with decision lists and the web":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decision lists for english and basque")
  Member "boosting applied to word sense disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no spare parts sharing part detectors for image categorization")
  Member "a winnow based approach to context sensitive spelling correction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a sequential model for multi class classification")
  Member "learning to resolve natural language ambiguities a unified approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine learning in automated text categorization")
  Member "forgetting exceptions is harmful in language learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory based shallow parsing")

Question: Please tell me which class the source paper 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)