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: "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
  Abstract: Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain.

Connected through hyperedge HE-1 (induced by source paper "human in the loop active covariance learning for improving prediction in small data sets"):
  with members:
    - "improving genomics based predictions for precision medicine through active elicitation of expert knowledge"
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "distributed representations of words and phrases and their compositionality"
    - "bayesian active distance metric learning"

Connected through hyperedge HE-2 (induced by source paper "improving genomics based predictions for precision medicine through active elicitation of expert knowledge"):
  with members:
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "regression with n 1 by expert knowledge elicitation"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "expectation propogation for the generative aspect model"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-3 (induced by source paper "user modelling for avoiding overfitting in interactive knowledge elicitation for prediction"):
  with members:
    - "improving genomics based predictions for precision medicine through active elicitation of expert knowledge"
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "interactive prior elicitation of feature similarities for small sample size prediction"

Connected through hyperedge HE-4 (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"
    - "preference elicitation for general random utility models"
    - "expectation propagation for approximate bayesian inference"
    - "expectation propogation for the generative aspect model"

Connected through hyperedge HE-5 (induced by source paper "interactive prior elicitation of feature similarities for small sample size prediction"):
  with members:
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "bayesian active distance metric learning"

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)