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 prior elicitation of feature similarities for small sample size prediction"
  Abstract: Regression under the "small $n$, large $p$" conditions, of small sample size $n$ and large number of features $p$ in the learning data set, is a recurring setting in which learning from data is difficult. With prior knowledge about relationships of the features, $p$ can effectively be reduced, but explicating such prior knowledge is difficult for experts. In this paper we introduce a new method for eliciting expert prior knowledge about the similarity of the roles of features in the prediction task. The key idea is to use an interactive multidimensional-scaling (MDS) type scatterplot display of the features to elicit the similarity relationships, and then use the elicited relationships in the prior distribution of prediction parameters. Specifically, for learning to predict a target variable with Bayesian linear regression, the feature relationships are used to construct a Gaussian prior with a full covariance matrix for the regression coefficients. Evaluation of our method in experiments with simulated and real users on text data confirm that prior elicitation of feature similarities improves prediction accuracy. Furthermore, elicitation with an interactive scatterplot display outperforms straightforward elicitation where the users choose feature pairs from a feature list.

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 elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "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 elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "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 elicitation of knowledge on feature relevance improves predictions in small data sets"

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 elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "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 elicitation of knowledge on feature relevance improves predictions in small data sets"):
  with members:
    - "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
    - "regression with n 1 by expert knowledge elicitation"
    - "why should i trust you explaining the predictions of any classifier"

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.LG(Machine Learning)