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: "learning to resolve natural language ambiguities a unified approach"
  Abstract: We analyze a few of the commonly used statistics based and machine learning algorithms for natural language disambiguation tasks and observe that they can be re-cast as learning linear separators in the feature space. Each of the methods makes a priori assumptions, which it employs, given the data, when searching for its hypothesis. Nevertheless, as we show, it searches a space that is as rich as the space of all linear separators. We use this to build an argument for a data driven approach which merely searches for a good linear separator in the feature space, without further assumptions on the domain or a specific problem. #R##N#We present such an approach - a sparse network of linear separators, utilizing the Winnow learning algorithm - and show how to use it in a variety of ambiguity resolution problems. The learning approach presented is attribute-efficient and, therefore, appropriate for domains having very large number of attributes. #R##N#In particular, we present an extensive experimental comparison of our approach with other methods on several well studied lexical disambiguation tasks such as context-sensitive spelling correction, prepositional phrase attachment and part of speech tagging. In all cases we show that our approach either outperforms other methods tried for these tasks or performs comparably to the best.

Connected through hyperedge HE-1 (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"
    - "boosting applied to word sense disambiguation"

Connected through hyperedge HE-2 (induced by source paper "a sequential model for multi class classification"):
  with members:
    - "the use of classifiers in sequential inference"
    - "a classification approach to word prediction"
    - "a winnow based approach to context sensitive spelling correction"
    - "a system for induction of oblique decision trees"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-3 (induced by source paper "the use of classifiers in sequential inference"):
  with members:
    - "a learning approach to shallow parsing"
    - "a winnow based approach to context sensitive spelling correction"

Connected through hyperedge HE-4 (induced by source paper "using the distribution of performance for studying statistical nlp systems and corpora"):
  with members:
    - "more accurate tests for the statistical significance of result differences"
    - "a learning approach to shallow parsing"

Connected through hyperedge HE-5 (induced by source paper "a classification approach to word prediction"):
  with members:
    - "a learning approach to shallow parsing"
    - "measures of distributional similarity"
    - "a winnow based approach to context sensitive spelling correction"
    - "expoiting syntactic structure for language modeling"
    - "similarity based models of word cooccurrence probabilities"

Connected through hyperedge HE-6 (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"
    - "boosting applied to word sense disambiguation"
    - "a winnow based approach to context sensitive spelling correction"
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-7 (induced by source paper "applying system combination to base noun phrase identification"):
  with members:
    - "noun phrase recognition by system combination"
    - "a learning approach to shallow parsing"
    - "forgetting exceptions is harmful in language learning"

Connected through hyperedge HE-8 (induced by source paper "a learning approach to shallow parsing"):
  with members:
    - "a winnow based approach to context sensitive spelling correction"

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)