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 classification approach to word prediction"):
  Abstract: The eventual goal of a language model is to accurately predict the value of a missing word given its context. We present an approach to word prediction that is based on learning a representation for each word as a function of words and linguistics predicates in its context. This approach raises a few new questions that we address. First, in order to learn good word representations it is necessary to use an expressive representation of the context. We present a way that uses external knowledge to generate expressive context representations, along with a learning method capable of handling the large number of features generated this way that can, potentially, contribute to each prediction. Second, since the number of words ``competing'' for each prediction is large, there is a need to ``focus the attention'' on a smaller subset of these. We exhibit the contribution of a ``focus of attention'' mechanism to the performance of the word predictor. Finally, we describe a large scale experimental study in which the approach presented is shown to yield significant improvements in word prediction tasks.

Includes members:
  Member "a learning approach to shallow parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "memory based shallow parsing")
  Member "measures of distributional similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the jensen shannon symmetrization of distances relying on abstract means")
  Member "a winnow based approach to context sensitive spelling correction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "machine learning in automated text categorization")
  Member "expoiting syntactic structure for language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "palm a hybrid parser and language model")
  Member "similarity based models of word cooccurrence probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentence entailment in compositional distributional semantics")

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)