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: "sequential update of bayesian network structure"
  Abstract: There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network structure is still an open problem. In this paper, we investigate sequential update of Bayesian networks were both parameters and structure are expected to change. We introduce a new approach that allows for the flexible manipulation of the tradeoff between the quality of the learned networks and the amount of information that is maintained about past observations. We formally describe our approach including the necessary modifications to the scoring functions for learning Bayesian networks, evaluate its effectiveness through an empirical study, and extend it to the case of missing data.

Connected through hyperedge HE-1 (induced by source paper "learning factored markov decision processes with unawareness"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "the bayesian structural em algorithm"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-2 (induced by source paper "online machine learning in big data streams"):
  with members:
    - "computing web scale topic models using an asynchronous parameter server"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "clipper a low latency online prediction serving system"
    - "xgboost a scalable tree boosting system"
    - "general factorization framework for context aware recommendations"
    - "vht vertical hoeffding tree"
    - "evaluation of recommender systems in streaming environments"
    - "optimal and adaptive algorithms for online boosting"

Connected through hyperedge HE-3 (induced by source paper "reasoning about unforeseen possibilities during policy learning"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "grounding symbols in multi modal instructions"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "training an adaptive dialogue policy for interactive learning of visually grounded word meanings"
    - "the bayesian structural em algorithm"
    - "representation learning a review and new perspectives"
    - "reasoning about knowledge of unawareness revisited"
    - "learning to learn"

Connected through hyperedge HE-4 (induced by source paper "parameter priors for directed acyclic graphical models and the characterization of several probability distributions"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "a transformational characterization of equivalent bayesian network structures"
    - "learning gaussian networks"
    - "learning bayesian networks a unification for discrete and gaussian domains"
    - "operations for learning with graphical models"

Connected through hyperedge HE-5 (induced by source paper "aggregating learned probabilistic beliefs"):
  with members:
    - "the topological fusion of bayes nets"
    - "some complexity considerations in the combination of belief networks"
    - "graphical representations of consensus belief"
    - "theory refinement on bayesian networks"
    - "belief revision a critique"

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)