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: "evaluating variable length markov chain models for analysis of user web navigation sessions"
  Abstract: Markov models have been widely used to represent and analyse user web navigation data. In previous work we have proposed a method to dynamically extend the order of a Markov chain model and a complimentary method for assessing the predictive power of such a variable length Markov chain. Herein, we review these two methods and propose a novel method for measuring the ability of a variable length Markov model to summarise user web navigation sessions up to a given length. While the summarisation ability of a model is important to enable the identification of user navigation patterns, the ability to make predictions is important in order to foresee the next link choice of a user after following a given trail so as, for example, to personalise a web site. We present an extensive experimental evaluation providing strong evidence that prediction accuracy increases linearly with summarisation ability.

Connected through hyperedge HE-1 (induced by source paper "retrospective higher order markov processes for user trails"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "linear additive markov processes"
    - "higher order organization of complex networks"
    - "general tensor spectral co clustering for higher order data"
    - "the new data and new challenges in multimedia research"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "tribeflow mining predicting user trajectories"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"

Connected through hyperedge HE-2 (induced by source paper "linear additive markov processes"):
  with members:
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "recurrent neural network regularization"

Connected through hyperedge HE-3 (induced by source paper "how users explore ontologies on the web a study of ncbo s bioportal usage logs"):
  with members:
    - "discovering and characterizing mobility patterns in urban spaces a study of manhattan taxi data"
    - "how to apply markov chains for modeling sequential edit patterns in collaborative ontology engineering projects"
    - "discovering beaten paths in collaborative ontology engineering projects using markov chains"
    - "hyptrails a bayesian approach for comparing hypotheses about human trails on the web"
    - "enabling semantic analysis of user browsing patterns in the web of data"
    - "user modeling combining access logs page content and semantics"

Connected through hyperedge HE-4 (induced by source paper "how to apply markov chains for modeling sequential edit patterns in collaborative ontology engineering projects"):
  with members:
    - "discovering beaten paths in collaborative ontology engineering projects using markov chains"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-5 (induced by source paper "discovering beaten paths in collaborative ontology engineering projects using markov chains"):
  with members:
    - "sequential usage patterns in collaborative ontology engineering projects"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-6 (induced by source paper "detecting memory and structure in human navigation patterns using markov chain models of varying order"):
  with members:
    - "how random are online social interactions"
    - "novelty and social search in the world wide web"

Connected through hyperedge HE-7 (induced by source paper "sequential usage patterns in collaborative ontology engineering projects"):
  with members:
    - "discovering beaten paths in collaborative ontology engineering projects using markov chains"

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.AI(Artificial Intelligence)