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: "preference elicitation for general random utility models"
  Abstract: This paper discusses {General Random Utility Models (GRUMs)}. These are a class of parametric models that generate partial ranks over alternatives given attributes of agents and alternatives. We propose two preference elicitation scheme for GRUMs developed from principles in Bayesian experimental design, one for social choice and the other for personalized choice. We couple this with a general Monte-Carlo-Expectation-Maximization (MC-EM) based algorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood functions for a class of GRUMs. We examine the performance of various criteria by experimental studies, which show that the proposed elicitation scheme increases the precision of estimation.

Connected through hyperedge HE-1 (induced by source paper "regret minimization in stochastic contextual dueling bandits"):
  with members:
    - "combinatorial bandits with relative feedback"
    - "pac ranking from pairwise and listwise queries lower bounds and upper bounds"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "multi dueling bandits with dependent arms"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "data driven rank breaking for efficient rank aggregation"
    - "double thompson sampling for dueling bandits"

Connected through hyperedge HE-2 (induced by source paper "minimizing time to rank a learning and recommendation approach"):
  with members:
    - "composite marginal likelihood methods for random utility models"
    - "learning mixtures of plackett luce models"
    - "learning mixtures of ranking models"
    - "noisy sorting without resampling"
    - "vote elicitation complexity and strategy proofness"

Connected through hyperedge HE-3 (induced by source paper "modeling spread of preferences in social networks for sampling based preference aggregation"):
  with members:
    - "a survey and taxonomy of graph sampling"
    - "discrete signal processing on graphs"
    - "efficient computation of the shapley value for game theoretic network centrality"

Connected through hyperedge HE-4 (induced by source paper "composite marginal likelihood methods for random utility models"):
  with members:
    - "a cost effective framework for preference elicitation and aggregation"
    - "computational and statistical tradeoffs in learning to rank"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "random utility theory for social choice"

Connected through hyperedge HE-5 (induced by source paper "a cost effective framework for preference elicitation and aggregation"):
  with members:
    - "composite marginal likelihood methods for random utility models"
    - "bayesian optimal active search and surveying"

Connected through hyperedge HE-6 (induced by source paper "towards non parametric learning to rank"):
  with members:
    - "learning mixtures of plackett luce models"
    - "random utility theory for social choice"

Connected through hyperedge HE-7 (induced by source paper "new models and methods for formation and analysis of social networks"):
  with members:
    - "formation of stable strategic networks with desired topologies"
    - "the role of social networks in information diffusion"
    - "incentive compatible influence maximization in social networks and application to viral marketing"
    - "competitive contagion in networks"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"):
  with members:
    - "regression with n 1 by expert knowledge elicitation"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "expectation propagation for approximate bayesian inference"
    - "expectation propogation for the generative aspect model"

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)