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 sequential model for multi class classification"):
  Abstract: Many classification problems require decisions among a large number of competing classes. These tasks, however, are not handled well by general purpose learning methods and are usually addressed in an ad-hoc fashion. We suggest a general approach -- a sequential learning model that utilizes classifiers to sequentially restrict the number of competing classes while maintaining, with high probability, the presence of the true outcome in the candidates set. Some theoretical and computational properties of the model are discussed and we argue that these are important in NLP-like domains. The advantages of the model are illustrated in an experiment in part-of-speech tagging.

Includes members:
  Member "the use of classifiers in sequential inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "a classification approach to word prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "p model alternative to the t model")
  Member "a winnow based approach to context sensitive spelling correction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the use of classifiers in sequential inference")
  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 "a system for induction of oblique decision trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards interpretable deep neural networks an exact transformation to multi class multivariate decision trees")
  Member "solving multiclass learning problems via error correcting output codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crowd scene analysis by output encoding")

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.AI(Artificial Intelligence)