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: "knowledge elicitation via sequential probabilistic inference for high dimensional prediction"
  Abstract: Prediction in a small-sized sample with a large number of covariates, the “small n, large p” problem, is challenging. This setting is encountered in multiple applications, such as in precision medicine, where obtaining additional data can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the relevance of the covariates, or of values of the regression coefficients, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of the proposed method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert.

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"
    - "interactive prior elicitation of feature similarities for small sample size 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 "expert guided regularization via distance metric learning"):
  with members:
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-3 (induced by source paper "improving genomics based predictions for precision medicine through active elicitation of expert knowledge"):
  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 propogation for the generative aspect model"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-4 (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"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"

Connected through hyperedge HE-5 (induced by source paper "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"):
  with members:
    - "regression with n 1 by expert knowledge elicitation"
    - "interactive prior elicitation of feature similarities for small sample size prediction"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "interactive prior elicitation of feature similarities for small sample size prediction"):
  with members:
    - "interactive elicitation of knowledge on feature relevance improves predictions in small data sets"
    - "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)